Handling regional research, development and innovation (RDI) disparities in Hungary: New measures of university-based innovation ecosystem

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Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
Handling regional research, development and
innovation (RDI) disparities in Hungary: New
measures of university-based innovation ecosystem
Zoltán Birkner
National Research,
Development and Innovation
Office,
University of Pannonia,
Hungary
E-mail: zoltan.birkner@nkfih.gov.hu
Ádám Mészáros
IFUA Horváth & Partners Ltd.,
Hungary
E-mail:
adam.meszaros2@
horvath-parterns.com
István Szabó
National Research,
Development and Innovation
Office,
Hungary
E-mail: istvan.szabo@nkfih.gov.hu
Keywords:
innovation policy,
smart specialization strategy,
regional inequalities,
regional innovation system
A
ccording to the Nomenclature of Territorial
Units for Statistics (NUTS2) level data from
Eurostat and the Regional Innovation
Scoreboard (RIS), a strong geographical
concentration in the field of RDI can be
observed in Hungary. Regional disparities are
more significant than in most European
Union (EU) member countries and have no
t
changed for a long time. The development o
f
regional innovation capacity is a fundamental
aspect from an economic policy point o
f
view; therefore, the Hungarian RDI polic
y
considers higher education institutions tha
t
play a ma
j
or role in the region and have a
strong knowledge production capacity, as
potential key players in knowledge transfer.
A
ccording to the intervention logic of the
introduced new measures, university-based
project- and system-level programs launched
can have an impact on local actors through
several channels. The university’s knowledge
base can be exploited, local businesses can
increase their competitiveness through access
to technology and RDI services, and their
innovation performance can be improved.
T
he key result of this study, based on a revie
w
of innovation policy measures, is to sho
w
how the new programs launched and the ne
w
institutions created under this ne
w
innovation policy paradigm will contribute to
a longer and more sustainable way o
f
reducing regional disparities in RDI
capacities and enhance regional innovation
performance through their impact on
economic develo
p
ment.

28 Zoltán Birkner – Ádám Mészáros – István Szabó
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
Introduction
The issue of regional innovation capacity in recent times, in line with the application
of smart specialization strategies (Foray 2014, 2016, Foray et al. 2018) and the
prominence of regional aspects of the innovation system as an approach (OECD
2007, Lundvall 2007) (Cooke 1992, Varga 2021) became increasingly appreciated.
Innovation performance plays a significant role in increasing the competitiveness and
productivity of enterprises (Halpern–Muraközy 2012, Mansury–Love 2008). This
approach is reflected in the fact that Policy Objective 1 of the EU Cycle 2021–2027
addresses innovation, enterprise development, and digitalisation closely together
(European Parliament and European Council 2021).
In recent times, innovation policy focuses on the actors and factors that interact
with each other, placing their interrelated system, that is, the innovation system, at
the forefront (Edquist 2005, Fagerberg–Sapprasert 2011). Consequently, innovation
policy not only deals with individual actors, but also focuses on their interactions. The
national innovation system as a model involves various actors (such as higher
education institutions, large companies, small and medium-sized enterprises (SMEs),
and local and central government), institutions (rules, norms, etc.), and relationships
(interactions) at both the national and regional levels. Regional inequalities in RDI
exists in all countries (in many cases, significant); however, significant regional
differences in Hungarian RDI performance in international comparisons draw
attention to the enforcement of the regional aspect in Hungarian innovation policy.
This study analyses regional inequalities, their development, and new policy
responses in relation to Hungary. The Hungarian case is remarkable in three aspects:
1) The Research, Development and Innovation Strategy 2021–2030 and the National
Smart Specialization Strategy 2021–2027 were accepted by the Hungarian
government in mid-2021, and these documents laid down the strategic framework for
the new decade. 2) The new programming period of the European Union for 2021–
2027 enables the financing of many of the analysed programs and institutions. 3) The
paradigm shift (i.e. the application of the university-based innovation ecosystem
approach and the initial implementation of programs according to this new concept)
allowed us to examine the intervention logic of the new measures.
Our goal is to analyse the inequalities of the RDI system of Hungary at the county
and regional levels and their dimensions, and to present the intervention logic of the
tools that, by influencing local RDI actors through university knowledge bases, create
the opportunity to exploit local strengths and opportunities, thereby potentially
reducing regional inequalities. Hungary's innovation policy begins with the approach
of the national innovation system and aims to create a university-centred innovation
ecosystem. Our basis is that connections with other actors can boost the innovation
performance of enterprises if they are primarily linked to technologically related and
technologically similar organisations (Broekel–Boschma 2016). This study examines

Handling regional research, development and innovation (RDI) disparities
in Hungary, 2014–2021: New measures of university-based innovation ecosystem 29
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
how policy measures to create a university-centered innovation system can contribute
to the development of regional innovation capacities, thus reducing regional
disparities. Owing to the novelty of the programs, their aim and content are not
described in the scientific literature, and their intervention logic regarding regional
aspects has never been analysed before.
Regional inequalities in research and development (R&D) and
innovation performance of Hungary in international comparison
The issue of regional disparities (e.g. income, growth, unemployment, or
convergence) has been the subject of a number of empirical studies analysing the
situation in the European Union for a long time (Camagni et al. 2020). Measuring the
regional distribution of particular socioeconomic parameters is a vital tool for
demonstrating the existence of regional disparities. There are sophisticated tools for
that (Spiezia 2003, Villaverde–Maza 2009, Lukovics 2009,; Tvrdoň–Skokan 2011),
and a number of papers dealt with several aspects of regional inequalities and
convergence of Hungarian regions (Szabó 2017, Egri–Tánczos 2018, Budai–Tózsa
2020, Demeter 2020); however, to achieve the objective of this study (to analyse how
to handle regional disparities with policy interventions) it is sufficient to use R&D
expenditures in proportion of gross domestic product (GDP) and the summary
innovation index (SII) of RIS to compare the Hungarian disparities in international
dimension and analyse these trends in an extended time frame.
In Hungary, R&D performance is highly concentrated in the European
comparisons. Based on this, we can obtain an idea to compare the R&D intensity of
the NUTS2 regions with the region of the highest R&D intensity, which is measured
by the ratio of gross expenditure on R&D (GERD) to GDP.
In most countries, regions including the capital city, are the most R&D intensive.
However, there were two exemptions: Belgium and the Netherlands. In Belgium, the
most R&D-intensive region was the province of Brabant Wallon (GERD/GDP:
7.67%), and not the capital region (Région de Bruxelles-Capitale). The high
GERD/GDP ratio is presumably based on the performance of the University of
Louvain, the Louvain-la-Neuve Science Park which is developing cooperation
between industry and the university, and a leading global pharmaceutical company
located in the region. In the Netherlands, according to Eurostat data, not the North
Netherlands region (Noord-Nederland, including the capital Amsterdam) but the
South Netherlands (Zuid-Nederland), including Eindhoven (with a number of
important research capacities, including the largest electronic companies’ research
centre) had the highest GERD/GDP ratio.

30 Zoltán Birkner – Ádám Mészáros – István Szabó
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
Figure 1
GERD/GDP ratios in the most R&D intensive region of the country and
on country level and their ratio in 2018
Notes: The GERD/GDP ratios are the latest data published by Eurostat: from 2017 for Austria, Belgium,
Germany, and Sweden, from 2013 for France, and from 2015 for Ireland.
Source: Eurostat GERD by sector of performance and NUTS2 regions [rd_e_gerdreg], own calculations.
In Sweden, Germany, Austria, and Slovenia, the differences were the lowest
among the most R&D intensive NUTS2 regions and the national average. In Hungary
and Budapest, there was a 67.6% difference. Belgium, Poland, Romania, and Slovakia
had a higher geographical concentration of R&D than Hungary.1 Thus, it can be
stated that R&D expenditure in Hungary is highly concentrated in the European
comparison (Figure 1).2
1 It should be noted that we will achieve similar result by measuring the concentration of R&D expenditure, that
is, comparing the share of R&D expenditure and GDP in the most R&D intensive regions.
2 In the case of small countries (Cyprus, Estonia, Latvia, Luxembourg, and Malta) NUTS2 level is missing and
the Eurostat publishes only country level data, therefore we could not calculate these ratios for the five countries. In

Handling regional research, development and innovation (RDI) disparities
in Hungary, 2014–2021: New measures of university-based innovation ecosystem 31
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
The RIS, modelled after the European innovation scoreboard (EIS), summarizes
and compares the innovation performance of European regions, and by utilising it,
we can also analyse the relative situation of Hungarian regions. As RIS has been
published since 2000 (Hollanders 2009), it is also a suitable tool for tracking changes
in performance across regions over time. RIS 2021 presents data for 240 regions in
22 EU Member States, while for small countries (Cyprus, Estonia, Latvia,
Luxembourg, and Malta) it introduces the country levels. Figure 2
Ratio of Summary Innovation Index of capitals or central regions
in percentage of country averages, 2021
Source: European Commission (2021a) own calculations.
Similar to the method applied for R&D, we can compare the innovation
performance of the most innovative regions of the countries and their averages. RIS
measures the innovation performance of regions using the SII. It is based on 32
the case of Austria, only NUTS1 level regions were published in RIS and by Eurostat and for the Netherlands in the
case of R&D expenditures we used NUTS1 data as the last NUTS2 level data were from 2012. Croatia changed the
geographical classification from 2021; therefore, in the 2018 GERD/GDP dataset we can find Kontinentalna
Hrvatska and in the later published RIS Grad Zagreb (which is a part formerly used Kontinentalna Hrvatska). Data
for Ireland are hard to compare because the geographical areas of the two datasets are not comparable. In case of
Finland, Etela-Suomi region includes Helsinki-Uusimaa.

32 Zoltán Birkner – Ádám Mészáros – István Szabó
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
indicators that are grouped into four main types: framework conditions, investments,
innovation activities, and impacts (Figure 2).
According to the RIS data, we can observe that the innovation performance of
Budapest outperforms the national average significantly (by 46.6%). Only in Poland
and Romania the differences between the innovation performance of capitals or
central regions are higher than in Hungary.3
The differences in the two data series (i.e. R&D and innovation performance)
manifest in two important aspects: in terms of the distribution of variables and
significantly different positions of some countries in the two rankings. Regarding the
distribution, we can observe that the ranges are different: they vary from 110–287.3%
in the dataset of R&D/GDP ratios and 104–186.3% in the RIS values. The standard
deviation is 42.8% in the case of R&D expenditures and only 19.4% in the values of
relative RIS performance).
Both phenomena can be explained by the same reason. Although there is a clear
connection between the two indicators (R&D performance is part of the RIS as R&D
expenditures are presented by two indicators: R&D expenditures in the public and
business sectors), RIS measures the performance of the entire innovation ecosystem,
and not only that of research units (research institutions, higher education institutions,
and businesses) conducting R&D. The RIS has 21 indicators (related to education,
digitalisation, venture capital, information technologies, intellectual assets,
employment and sales impacts, environmental sustainability, and innovation) and
measures a wide range of actors. It is clear that the R&D performance indicator is
based on the activity of fewer actors compared to RIS, as in the first one, only R&D
expenditures of research units are measured.
An important characteristic of R&D is that, in most cases, it is concentrated in
large firms.4 Consequently, the geographical distribution of the largest research units
matters: some of them might have a significant impact on the performance of the
region: the higher standard deviation and the large differences between the
performance of the central region and the national average can be caused by this
factor, particularly in the case of smaller countries. Another consequence is that in
the Netherlands and Belgium, the capital regions are not the most R&D intensive,
and some case differences can be observed in innovation and R&D performance.
Following the performance of Hungary's regions, it can be observed that no
significant progress has been made since our accession to the EU, however the
changing nomenclature and number of categories have also affected the classification
3 Similar to the GERD/GDP data, RIS Summary Innovation Index is only available for country level in the case
of Cyprus, Estonia, Latvia, Luxembourg, and Malta.
4 As statistical offices do not publish data on the level of research units, we can only indirectly verify our
hypothesis on the level of concentration. According to the analyses of Hungarian Central Statistical Office (HCSO
2020a), more than two-thirds of the total national economy’s R&D expenditure was spent by the top 100 enterprises
in 2019. As the total number of business research units was 2082 in that year, a very high level of concentration was
observed.

Handling regional research, development and innovation (RDI) disparities
in Hungary, 2014–2021: New measures of university-based innovation ecosystem 33
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
of Hungarian regions, mainly causing temporary shifts. In 2004, RIS used five
categories (high innovators, medium-high innovators, average innovators, medium-
low innovators, and low innovators). Central Hungary was in the average innovator
category, and our other regions were in the group of low innovators. In 2006, Central
Transdanubia was placed in the medium-low innovators category, while the other
Hungarian regions remained in the low innovators category, and the situation in
Central Hungary did not change [1]. In 2012, EIS used only four categories
(innovation leader, innovation follower, moderate innovator, and modest innovator).
Central Hungary was placed in the moderate innovator category, while the other
regions became modest innovators [2]. In 2014, Southern Transdanubia, Northern
Hungary, and the Northern Great Plain belonged to the modest innovator group,
whereas the others belonged to the moderate innovator group [3].
In 2017, RIS introduced another type of categorisation, dividing each of the
former four categories into three additional groups: the best one-third with a “+” sign
and the weakest one-third with a “-” sign. In this study, Central Hungary was marked
as moderate +, Southern Great Plain and Central Transdanubia as moderate, and
others as moderate [4]. In 2019, Budapest and Pest counties were measured separately
(moderate +), the Northern Great Plain received only modest +, and the other
regions received moderate [5].
The 2021 report [6], in line with the methodological changes of the EIS, introduced
the emerging innovator category instead of the modest category and linked the
categorisation to a strict performance relative to the EU average (European
Commission 2021b). Here, Budapest belongs to the moderate + group, Pest, Central
Transdanubia, Western Transdanubia, and the Southern Great Plain belong to the
emerging + category, while the other regions belong to the emerging group (Figure 3).
Owing to methodological changes, the development of the regions cannot be
compared over time for the entire period; however, the RIS interactive database
shows the development of the performance between 2014–2021, compared to the
EU average. The performance of Budapest and Pest county changed the most,
Budapest’s rose from 88.81% to 115.75% of the EU average in 2014, Pest county’s
from 48.97% to 68.31%. As a result of the performance gains in 2021, the
performance of the Northern Great Plain has increased significantly (but still
remained below the half of the EU average of 2014) and Central Transdanubia rose
by 12.9%. The performance of other Hungarian regions has changed only slightly
since 2014 compared to the EU average of 2014.

34 Zoltán Birkner – Ádám Mészáros – István Szabó
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
Figure 3
Change in relative performance of Hungarian NUTS2 regions to the EU average of
2014
Source: European Commission (2021a).
Regional inequalities in R&D and innovation performance –
comparison of Hungarian regions
If we compare the performance of Hungarian regions with each other and measure
the dynamics of Budapest relative to other countries, we can use the same methods
as in the previous section on international comparison.
We observe significant inequalities when comparing the amount of R&D
expenditure as a proportion of GDP at the regional level (in this case, county data are
also available). Above the Hungarian average in 2018 (1.51%) are Veszprém (3.44%),
Budapest (2.53%), Csongrád-Csanád (2.34%), and Hajdú-Bihar (1.86) counties,
however the performance of Baranya (1.03%), Pest (0.92%), and Győr-Moson-
Sopron (0.89%) can also be mentioned. All of these are counties with large university
centres. There are eight counties in which this indicator does not reach the value of
0.5%, which indicates low R&D performance of these regions (Figure 4).

Handling regional research, development and innovation (RDI) disparities
in Hungary, 2014–2021: New measures of university-based innovation ecosystem 35
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
Figure 4
GERD/GDP by Hungarian counties in 2018
Source: [9].
The concentration is indicated by the fact that the share of Budapest has remained
unchanged for a long time, apart from some minor fluctuations in R&D expenditures
and the number of researchers: according to the Hungarian Central Statistical Office
(HCSO) data, the capital accounted for 60.2% of R&D expenditures in 2004, and in
2019 this rate remained almost unchanged at 60.6%.
To measure the concentration of R&D expenditures in Hungary, we used the
Herfindahl–Hirschman index (HHI) to scale its dynamics. As we consider county
level R&D expenditure data of the HCSO, we observed that it was 0.41 in 2004 and
0.39 in 2019 (fluctuating between 0.35 and 0.45 in this period). This indicates that
during the one and a half decades, no significant changes could be observed in terms
of the concentration of R&D performance in Hungary.
According to the RIS data, regional disparities in the level of innovation have
increased since 2014. The only exception is Pest County, which performed slightly
better than Budapest, while the performance of other regions, although to a different
extent, deteriorated compared to the capital; this is shown in Table 1.
Supporting business innovation is key to the Hungarian RDI policy. The
proportion of innovative companies has been “traditionally” low for a long time, and
it is no coincidence that this is one of the weakest points of Hungarian RDI
performance, as confirmed by the European Commission’s country reports published

36 Zoltán Birkner – Ádám Mészáros – István Szabó
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in the framework of the European Semester (European Commission 2020). It is
problematic that the majority of Hungarian enterprises, particularly SMEs, do not
innovate, and the proportion of innovative companies shows an unequal regional
picture. Furthermore, a significant proportion of Hungarian companies do not see a
reason for innovation. According to the Community Innovation Survey (CIS), 86.5%
of non-innovative Hungarian companies with at least 10 employees do not have a
significant barrier to innovation, and most of these companies are likely to see no
point in performing innovation. Table 1
Performance of Hungarian regions relative to Budapest
(%)
Year Pest County Central
Trans-
danubia
Western
Trans-
danubia
Southern
Trans-
danubia
Northern
Hungary
Northern
Great Plain
Southern
Great Plain
2014 55,1 55,8 54,4 46,4 44,3 41,5 57,7
2015 54,7 51,8 46,6 41,7 35,6 39,3 51,3
2016 59,4 50,8 50,9 39,2 36,3 40,7 51,1
2017 54,6 49,5 41,7 38,2 34,8 41,0 48,5
2018 59,7 49,8 42,1 36,7 35,0 39,8 49,2
2019 59,8 47,9 48,7 37,6 40,8 39,9 42,8
2020 58,2 45,1 44,0 39,7 39,0 38,1 47,2
2021 59,0 48,3 44,5 36,8 37,1 39,4 47,8
Source: European Commission (2021a) own calculations.
Supporting business innovation is also emphasised in the RDI Strategy of Hungary
for 2021–2030. Although innovation data with a territorial breakdown deeper than
the NUTS2 level are not available, it can be observed that the proportion of
innovative enterprises in Budapest is the highest (31.1%), while in most regions, their
share is approximately 20%. This indicates a relative weakness in the innovation
capacities of the latter regions.
If we raise the question whether the disparities are diminishing as a possible
consequence of the previously implemented policy measures, the picture is twofold.
We can observe a constant disparity in terms of innovation performance (measured
by RIS) between European regions and meanwhile a growing inequality between
Hungarian regions. The same can be observed if we calculate standard deviation for
GERD/GDP ratios between regions: the European data are constant while the
disparities between Hungarian regions grew between 2014 and 2019 (Figure 5).

Handling regional research, development and innovation (RDI) disparities
in Hungary, 2014–2021: New measures of university-based innovation ecosystem 37
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
Figure 5
Share of innovative enterprises by NUTS2 regions 2018
Source: [8].
Table 2
Standard deviation of RIS Summary Innovation Index and GERD/GDP ratios
between NUTS2 level EU regions and Hungarian regions
2014 2015 2016 2017 2018 2019 2020 2021
R&D/GDP standard
deviation: European regions 0.67 0.64 0.64 0.63 0.65 n.a. n.a. n.a.
R&D/GDP standard
deviation: Hungarian regions 0.37 0.50 0.42 0.40 0.47 0.44 n.a. n.a.
RIS Summary Innovation
Index standard deviation:
European regions 47.20 46.19 46.83 45.48 45.79 45.78 45.47 46.30
RIS Summary Innovation
Index standard deviation:
Hungarian regions 16.36 21.35 20.74 21.63 22.21 20.63 21.95 24.17
Source: European Commission (2021a) and Eurostat GERD by sector of performance and NUTS2 regions
[rd_e_gerdreg]..As the dataset of Eurostat „GERD by sector of performance and NUTS2 regions [rd_e_gerdreg]”
had a number of missing values, standard deviation is calculated only for regions which had data for all years of the
given period (we calculated standard deviation for 122 European regions).
This indicates that despite the original aims of the Cohesion Policy, Hungary faces
not only a high geographical concentration in the field of RDI and a high level of
regional disparity compared to other EU countries but also a negative trend of

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growing regional disparity within the country. This seeks the attention of
policymakers to develop a new policy framework to enhance the RDI performance
of all Hungarian regions.
Addressing regional inequalities in Hungarian innovation
policy – strategic level
Among the current Hungarian strategies, the National Research, Development and
Innovation Strategy 2021–2030 (hereinafter: RDI Strategy) and the National Smart
Specialization Strategy 2021–2027 (hereinafter, S3) address the reduction of territorial
inequalities in RDI. These strategies, in addition to establishing the professional side
of the allocation of domestic and EU funds, also provide a landscape of RDI and
identify the planned government measures regarding the goal that must be achieved.
In addition to these, the innovation aspects of territorial inequalities are also
reflected in general in the National Development and Regional Development
Concept, which deals with the issues of regional, social, and economic cohesion. It
outlines a long-term vision for 2030 based on Hungary's social, economic, sectoral,
and territorial development needs and sets development policy goals and principles.
In this study, because our aim is to examine regional inequalities in RDI, we present
the regional aspects of the RDI strategy and the targets of S3, as well as the regional
aspects of the latter’s methodology.
In the RDI Strategy for 2021–2030, the reduction of regional disparities specific
to the RDI system is markedly reflected, because the vision of the strategy is: „A high-
value-added, knowledge-based, balanced, sustainable economy, and society in all areas
of the country” (ITM 2021a: 27).
Consequently, one element of the horizontal objectives of the RDI strategy is to
strengthen regional, social, and economic cohesion through RDI policy measures. An
important goal is to encourage the innovation performance of regions outside
Budapest and to raise awareness that the ability to innovate is of utmost importance
for all economic actors. According to the RDI strategy, a critical mass of local and
regional RDI initiatives is essential to strengthen cohesion. Large cities and the
research, knowledge transfer, and economic organisations operating there can play a
major role and have a spill over effect on the entire region. The university-based
innovation ecosystem model is connected to the territorial capital model theory.
According to this concept, regional resources can be divided into two categories:
traditional and innovative. The former includes capital stock, natural and cultural
resources, infrastructure, and human and social capital. The second group includes
relationship capital, corporate and R&D cooperation, and networks (Tésits et al.
2021). The university-based regional development model can accelerate innovative
elements; however, the success of the policy depends on the readiness of traditional
elements and might cause bottlenecks despite the development of innovation policy

Handling regional research, development and innovation (RDI) disparities
in Hungary, 2014–2021: New measures of university-based innovation ecosystem 39
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measures. Establishing regional cooperation is a challenge: many moderately
developed regions show selective collaboration patterns in European Framework
Programs, mostly with an external focus, as stated in a study of European NUTS3
regions (Sebestyén et al. 2021). Based on the participant-level network in our analysis,
through the waves of the framework programs of FP5, FP6, and FP7, the study
showed that while the university-based model of research cooperation appears to be
well-established in more developed parts of Central-Eastern European countries
(including Hungary), other NUTS3 regions of Hungary showed different cooperation
patterns, some of which were non-cooperative and/or had low levels of local
cooperation.
A study (Grasselli 2006) on the Northern Great Plain region of Hungary showed
that despite the excellent capacities of the university, it was difficult to boost the
innovation performance of the region as certain parts of the institutional side were
missing, and the innovation approach of the public sector must be improved.
This requires the strengthening of regional knowledge centres, research, and
technological infrastructure. These organisations (mainly higher education
institutions as centres of innovation) should be encouraged to play a key role in the
RDI system. As many actors as possible should be given the opportunity to engage
in an innovative ecosystem.
It should be added, however, that territorial inequalities in research based on
excellence cannot be fully redressed; it is not a policy goal to achieve full convergence,
but rather to exploit comparative advantages. Owing to the unique characteristics,
strengths, and weaknesses of the regions, they can be successful in different ways, and
their actors can form a competitive regional innovation system by defining different
priorities and using different methods. This is also one of the central ideas of S3.
S3 is a policy tool that focuses on the development of specific, place-based, and
specialization-focused directions (Foray et al. 2009, European Commission 2012a, b),
the use of which has become common in the European Union in the period 2014–
2020, as S3 has become an ex ante condition for access to innovation resources
(European Union 2013).
From an innovation policy perspective, it is crucial to combine the expectation to
consider the actors of the innovation system and the aspiration of cohesion policy,
which seeks to set local strengths and opportunities into development focus. These
two concepts are combined by S3 (Foray 2014). Thus, S3 linked the spatial definition
of innovation and entrepreneurship with EU policies of regional aspects (including,
among others, industrial, cohesion, and innovation policies), thereby becoming a
regional development policy. S3 is a place-based policy tool that focuses on the
development of regions and specialisation directions.
In the 2021–2027 development cycle, the aim of smart specialisation strategies is
to contribute to the achievement of the policy objective of the Cohesion Policy called
„a smarter Europe by supporting innovation and economic transformation and

40 Zoltán Birkner – Ádám Mészáros – István Szabó
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modernization”. S3 can effectively support regional cohesion by integrating the fields
of RDI, digitalisation, and economic development, laying the foundation for the
efficient use of resources of the cohesion policy for the modernisation of regions.
Building on the strategic goals of the former three disciplines, S3 can be interpreted
as an umbrella strategy for these three areas (RDI, enterprise development, and
digitalisation). It fixes the specialisation directions and priorities that have great
development potential, which can become the basis for the intelligent transformation
of regions.
One of the most essential elements of S3 methodology is the application of the
entrepreneurial discovery process (EDP). During the EDP, in an interactive, bottom-
up manner, the regional actors who are important elements in the “quadruple helix”
at the territorial level, articulate the strengths of the region and the priorities, areas
and breakout opportunities they perceive (Foray 2016, McCann–Ortega-Argilés
2016). Hungary used the EDP methodology to prepare the current S3 as follows. A
key element of the EDP was the creation of regional innovation platforms (RIPs)
based on local university centres, initiated by the National Research, Development
and Innovation Office (NRDIO) and the Ministry for Innovation and Technology.
At the regional level, RIPs provide an opportunity for cooperation between higher
education, industry, central and local governments, and civil society, as well as for
cross-sectoral dissemination of the innovation process, the organisation of activities
related to the implementation of S3, and the development of proposals to achieve the
objectives. 5
As another EDP tool, the NRDIO assessed the suggestions and requirements of
RDI actors regarding smart specialisation and priorities between November 2019 and
March 2020 in the framework of an online questionnaire, which was filled in by 829
participants. 6 The established priorities were validated in November 2020 with the
help of 106 organisations representing all major players in the quadruple helix (ITM
2021b).
The EDP used in the framework of S3 thus ensured that the strategy and its
priorities were developed considering the actors of the innovation system and the
regional conditions, as well as the requirements and concepts of the local actors.
Defining bottom-up priorities is necessary for using the strengths of the local
innovation system with sufficient efficiency, thus reducing regional inequalities.
5 Within the framework of the EDP, RIPs were established in eight locations by June 2020 (Miskolc, Debrecen,
Győr, Pécs, Szeged, Budapest, Veszprém, and Gödöllő), more than 1,100 people participated in the conferences
establishing the platforms, then in 2021 five more (Nyíregyháza, Dunaújváros, Sopron, Eger, and Kecskemét) RIPs
were established (in an online format, owing to the pandemic).
6 34.6% of the respondents are from Budapest and 12.6% from Pest county; the share of Csongrád-Csanád,
Hajdú-Bihar, Veszprém, Győr-Moson-Sopron, Baranya, and Borsod-Abaúj-Zemplén counties is more than 4%
(owing the larger university cities), while the share is less than 1% in Jász-Nagykun-Szolnok, Nógrád, and Tolna
counties, in connection with the low RDI intensity and size of the latter.

Handling regional research, development and innovation (RDI) disparities
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The measures described in the next section are in line with the concept of system-
oriented innovation policy. This is the latest approach to innovation policies and aims
to improve the performance of the national innovation system, not only of individual
actors. Therefore, it is important to create sustainable institutionalised frameworks to
improve cooperation between actors and develop their skills (Edler–Fagerberg 2017).
The greatest improvement over the measures of the previous periods is that Hungary
overcame the principles of invention-oriented innovation policy, where the focus was
mostly on R&D. In this model, policymakers believe that the advancement of science
and R&D support can be beneficial (according to the linear model of innovation) for
the entire society. To overcome interventions that improve only the performance of
individual actors in the innovation ecosystem, most of the new measures concentrate
on establishing institutions which are able to formalise sustainable cooperation with
common interests and visions.
Addressing regional inequalities in Hungarian innovation
policy – new institutions and programs
According to an evaluation of RDI funds for operational programs from 2014–2020
(Ernst &Young 2020), Hungarian higher education institutions play an important role
in RDI cooperation. Compared to the period from 2007–2013, the relations of higher
education institutions in the field of RDI have expanded, both in terms of their
number and intensity. Despite this, the role of business financing in R&D at
universities has declined in recent years, and in 2007, the business enterprise sector
financed 13.7% of R&D in higher education institutions, 9.1% in 2014, and only
2.73% in 2020. This is a low ratio compared to the European average of 7.3% in 2019
[7], [9]).7 The general RDI trends are also twofold: despite the constant growing trend
of R&D expenditures in Hungary (both in absolute values and in terms of GDP), the
overall innovation performance of the country remained constant. According to the
European Innovation Scoreboard, performance relative to the EU has decreased over
time (70% in 2014 and 68% in 2021). As we observed in the previous section, this
statement is valid at the regional level as well; the development of innovation
performance of regions remained only minor. These trends highlight the fact that the
former policy measures could not reach the aim of giving a new impetus for
Hungarian innovation performance and were not able to raise the importance of
higher education institutions in the national innovation system.
The central goal of Hungarian innovation policy, according to which higher
education institutions can become fundamental actors in the dynamization of the
innovation system, is primarily based on the following observations: 1) Hungary has
significant university capacities, and the institutional system covers the entire country.
7 The explanation of this contradiction is that the report of Ernst &Young analysed the projects of institutions
involved in calls of operational programmes and the statistical office had data on all higher education institutions.

42 Zoltán Birkner – Ádám Mészáros – István Szabó
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2) In addition to traditional research relations, the presence of universities can
significantly improve the economic and social conditions of a particular region. 3)
Recent examples of the development of university-company relations have shown
that actors are able to develop fruitful collaborations, such as in the case of Centres
for Higher Education and Industrial Cooperation (NRDIO 2019); however, these are
project-like and island-like collaborations, with no general organizational or systemic
background.
On the contrary, the regulatory environment hinders entrepreneurial activity of
universities: the fragmented legal background and public procurement rules make it
hard for universities to have equal negotiating position as market players.
Remuneration frameworks, inflexibility of rules and legal environment are obstacles
of motivation, while the salary gap between the university and the private sector make
it hard to attract (or at least keep) talented professionals (Erdős 2019). According to
Bedő–Erdős (2021), among others, low entrepreneurial spirit, lack or low number of
local incubators, deficiencies in human resources, problems of social networks,
weakness of knowledge creation, and lack of access to resources can be considered
the main problems in this respect. Case studies of Central-Eastern European
universities (Bedő–Tolmayer 2021) also underline the importance of entrepreneurial
culture and institutional support for these activities.
The model change in Hungarian universities is a systematic answer to the
institution-related and regulatory part of the above-mentioned problems. Structural
changes can lead to a university-based innovation ecosystem with great flexibility and
another type of strategic thinking in higher education institutions. The new model
ownership approach generates greater responsibility and enables long-term planning.
Institutions can apply new motivational schemes and performance-based evaluations
(including new tasks and responsibilities derived from universities’ new roles). On the
contrary, institutions would continue to operate as non-budgetary organisations in a
more flexible regulatory environment (e.g. in the case of public procurement). These
changes will enable institutions to build successful and sustainable cooperation with
local actors (Bódis 2021).
Thus, a major higher education institution operating in a particular county or
region may provide a strong basis for the institution to have a positive impact on
corporate innovation in addition to strengthening R&D enterprises. Indeed, this
effect succeeds when universities conduct a wide range of third mission activities in
addition to their traditional activities (education and research), and all of these have
become top priorities for policy (Estrada et al. 2016). The three main dimensions of
university activities can be defined as a third mission: a) knowledge and technology
transfer, b) further education, and c) social engagement (Berghaeuser–Hoelscher
2020). In recent years, the third missionary initial approach, which was primarily a
passive, service-oriented approach, has been replaced by a more engaging, proactive
approach (Frondizi et al. 2019).

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The success of cooperation programs between higher education institutions and
businesses depends on a number of factors, and it is essential to build a concept in
which the partners receive greater benefits from their cooperation that contributes to
both their short-term and long-term objectives and the ability to bring research into
practice creating impact (Davey et al. 2018).
Although similar elements have been applied in Hungary in recent years, most
measures described in this study are new. Most of the new programs of the university-
based innovation ecosystem rely on the findings of theoretical frameworks and the
experiences of other countries; therefore, potential bottlenecks and planned measures
can be defined.
In Hungarian higher education institutions, everything is in place for success in
this regard. Certainly, they play an important role in training as well as supply of
human resources to enterprises, which, together with the companies’ core business
activity, now also considers the requirements of enterprises. Moreover, recent trends
have raised the requirement for so-called micro-credentials, that is, a part of the
training should conducted within an even shorter time frame and market-oriented
manner (Kato et al. 2019).
The dual training system introduced in higher education in September 2015, which
supports participants in undergraduate training in gaining an internship at a company
of their choice, simultaneously enables not only the most practice-oriented training
but also the company’s involvement in education, influencing its direction.
In countries with developed cooperation patterns between companies and
educational institutions (e.g. Germany, Austria, and Switzerland), the private sector
plays an important role in vocational education. The Hungarian model is based on
that of Baden-Württemberg, Germany, where the University of Cooperative
Education was established in 1974 (DHBW 2021) and has a long tradition and
expertise in this field and the introduced dual education system creates the
combination of on-the-job training and academic studies and, therefore, achieves a
close integration of theory and practice, both being components of cooperative
education (Ehlers et al. 2019). According to international experience, the main
challenge of dual education is to establish a system which is attractive to employers
and students. A literature review (Valiente–Scandurra 2017) concluded that
employers’ associations and chambers of commerce should be involved in the
governance of the programme to convince their members regarding its advantages.
According to this recommendation, in the supervisory body of this programme, in
the Hungarian Dual Education Council, two delegates from the Hungarian Chamber
of Commerce, one from the Chamber of Agriculture, and other from the Hungarian
Chamber of Engineers, participated.
As many corporate professionals are also involved in this process, cooperation
between businesses and higher education institutions is already being strengthened.
This, of course, also indicates that companies get to know the service activities of

44 Zoltán Birkner – Ádám Mészáros – István Szabó
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higher education institutions, that is, their various research and measurement
capacities as well as the related fields of teaching and research, which can be marketed
and transformed into services beyond traditional functions.
As can be observed, dual training is not primarily aimed at RDI-type
collaborations, although it can also provide a basis for these collaborations.
Simultaneously, the Cooperative Doctoral Program, which was created as an
extension of the logic of training, specifically considers corporate-higher education
cooperation for RDI purposes.
Apart from the „conventional” PhD degrees, different types of doctoral programs
are offered in other countries: professional doctorates in the Anglo-Saxon countries
aim to involve senior professionals; professional practice doctorate programmes are
not equal to PhD and are increasingly required by professional associations and
agencies to enter professional practice in the US. Industrial PhDs are common in
continental Europe (e.g. Germany, Denmark, Sweden, France, the UK, and Italy),
however their schemes and characteristics vary between countries (Ori 2013).
According to studies on their results (Kuhn 2011, Danish Agency for Science,
Technology and Innovation 2012, WASP 2019), it can improve the labour
productivity, patent activity, rate of employment, and level of wages of companies,
and can contribute to more effective technology, product development, and
prototype creation.
The main goal of the Hungarian Cooperative Doctoral Program is to support the
achievement of academic degrees by colleagues with corporate experience and
through them, strengthen the RDI activities of enterprises. It captures corporate RDI
at the doctoral student level, as participants in training receive not only scientific
knowledge in their training but also research topics that can explicitly be utilised by
their host company. The research topic and the research itself must, of course, meet
the expectations of academia and doctoral requirements, however by ensuring that
the doctoral student is assisted by a company expert in addition to the supervisor, it
is guaranteed that the utilisation of research results is realised.
A further benefit of the program is the presentation of RDI activities provided by
higher education institutions to enterprises through students, which may also increase
the current modest utilisation of the research infrastructure (Deák–Szabó 2016). The
indicators of the first year are extremely positive: 246 students received support, of
which 121 students appeared in the program, who are the primary target group of the
program; that is, they are already starting PhD training with corporate experience.
The KDP contributes to the reduction of territorial inequalities by providing a supply
of researchers in line with local requirements and the possibility of utilising scientific
results, so that the performance of local enterprises can increase significantly. As the
program started in September 2020, we cannot see further results, however the
planned assessment and revision of the RDI strategy and its action plan ensures that
the impacts and experiences of the program will be evaluated.

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In addition to innovation collaboration at the individual level, institutional
collaboration forms the basis of the new innovation paradigm, as highlighted in the
above strategies. Although higher education institutions are present in all regions, the
innovation activity of enterprises, as we have observed, is stagnant, and obvious
changes have been made in their operating model.
Accordingly, institutions receive funding through a variety of support schemes
designed to enable higher education to provide services for businesses in a one-stop-
shop, comprehensible, and business-like manner, and even more so by engaging with
them. The Thematic Excellence Program started in 2018 and specifically aimed at
enabling higher education institutions to identify areas for RDI activities in
interdisciplinary collaboration, similar to the logic of smart specialisation.
Research schemes similar to the Thematic Excellence Program and Competence
Centres are common in other countries; institutional funding of public research
projects and project-based cooperation can be observed in many OECD countries
(e.g. [10]). The antecedent of Thematic Excellence Program in Hungary was the
Support Scheme of Institutional Excellence: between 2013–2018 the selected
universities (titled with the status of ‘research university’) got horizontal funding
without defining research projects and therefore the program was not able to
concentrate resources on thematic fields.
RDI projects financed by the Thematic Excellence Program provides a strong
basis for institutions to establish collaborations between faculties and departments
instead of the silo-like functioning of individual and independent units within the
institution. In developing the concept, Hungary embraced the recognition of
Mazzucato’s (2018) mission-oriented research paradigm, that concentrates resources
and uses a problem-centric approach, thus adding the “challenges” logic of the
European Commission’s Horizon Europe program (European Commission 2021b)
to adapt to Hungarian conditions.
Under the program, 92 thematic studies from 27 higher education institutions and
public research institutes received support by 2020. The continuation of the program
ensures that institutions focus resources on large RDI areas, such as health, culture,
security, or industry. In the case of these projects, although cooperation with
businesses is not a priority, experience has demonstrated that this has been achieved
in many cases, helping develop local businesses.
The program of Competence Centres is a new form of the former Regional
University Knowledge Centre Program, which started in 2004. The latter aimed to
establish innovation centres to enable the institution to cooperate with local actors,
and the partners had to declare that they contributed to the establishment of the
centre and participated in their R&D activities (NKTH 2004). The main difference
between the two programs is that Competence Centres are project-based cooperation
and the involvement of partners is well-defined in the structure; therefore, the
establishment of a sustainable and result-oriented framework is possible. Competence

46 Zoltán Birkner – Ádám Mészáros – István Szabó
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Centres’ RDI projects are implemented with a range of enterprises on a dedicated
topic. (The previously established centres for higher education and industrial
cooperation have a similar intervention logic.) They create market-based RDI
capacities in higher education institutions that provide a modern research background
for corporate partners and create conditions for active and continuous cooperation,
ensuring a sustainable economic model even after the completion of the supported
project. The latter explicitly supports the possibility of utilising the results for other
companies that were not involved in the project, unlike in the case of a „traditional”
RDI project. Competence Centres thus create services and products based on
corporate requirements that can be integrated into the university’s service portfolio.
Simultaneously, as indicated, Competence Centres are project-based RDI
collaborations, as opposed to the concept of program-based cooperation, for which
science-and enterprise-oriented systems have been introduced. Because of the
potential regional effects of the former, the system of National laboratories, are
limited, in this study, we present an enterprise-oriented type of program-level
collaboration: Science and Innovation Parks to be established between 2021–2027.
Representing a new infrastructure that is also physically important in the field of
innovation will create the possibility of cooperation between local enterprises and
universities, as well as for the given RDI infrastructure to take place at a system level
(i.e. beyond the project level). The aim is for the Science and Innovation Parks in the
regional RDI space to bring together as many regional actors as possible in a physical
sense, while mutually taking advantage of synergies.
The concept of Science and Innovation parks is based on helix models, in which
the interaction and cooperation between the actors of the innovation system is a key
factor of success (Etzkowitz–Leydesdorff 2000, Carayannis–Campbell 2009,
Carayannis et al. 2012). A common feature of science parks is that they can facilitate
the transformation of scientific knowledge and research results into marketable
technologies and help the establishment and growth of technology-intensive
companies. They have four models according to McCarthy’s (1998) matrix. These
include landlords, matchmakers, coaches, and gardeners. McCarthy’s categorisation is
based on two dimensions: specialisation and level of services. As Science and
Innovation Parks or similar institutions have never been established in Hungary,
factors of success can be drawn only from international examples.
The conditions for operating successful science parks (based on studies by
Dinteren 2021, Benny 2021) can be divided into two groups. Factors of success at the
level of Science and Innovation Parks can be influenced by the innovation policy and,
in a large part, depend on the implementation at the institutional level. These are: 1)
embeddedness into the regional economy; 2) concept and strategy based on market
research; 3) clear profile of the SP: target groups and sectors; 4) optimal size, quality
of research, and service infrastructure; 5) well-organized and strong management; 6)
experience and skills of technology transfer offices; 7) professional, full-time, and

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constant management; and 8) infrastructure developed for specific target groups. The
second group of success factors are regional conditions that are hard to influence
within the framework of innovation policy: 1) scientific (university) bases; 2) existence
of effective networks of businesses and institutions; 3) skilled labour and regional
labour supply; 4) high level of urbanisation; 5) entrepreneurial culture, mentors, and
role models; 6) proximity to services and large companies; 7) sectoral structure of the
economy; 8) growing start-ups; and 9) adequate sources of funding (including regional
investors). The desired level of business involvement is the realisation of the following
advantages: the possibility of developing new products and services, cost savings, the
possibility of recruiting talent, cooperation with other institutions, and more effective
fundraising. The implementation of the Science and Innovation Parks has to pay
special attention to these factors, and raising awareness is an essential factor of
success.
Science and Innovation Parks are also the physical incarnations of the RIPs
described above. Participants in RIP can find a partner in both R&D and innovation,
whether in entrepreneurship, higher education, or research institutes, for the
implementation of their project. The results of Thematic Excellence Programs or
Competence Centres are also the most easily accessible to local actors. The structure
of cooperation between Science and Innovation Parks, as well as Regional Innovation
Platforms, enables the establishment of mutually beneficial innovation collaborations
based purely on requirements and competencies. Such collaborations can also
become internationally competitive and involve international partners.
However, in addition to the structure of the institutional system, it is important to
operate it, that is, to establish collaboration and build trust between the actors.
Currently, two main elements of this have been launched. On the one hand, the
above-mentioned Regional Innovation Platforms, which in each case have been
established on the basis of a higher education centre. On the other hand, the
University Innovation Ecosystem tender encourages higher education institutions to
make their RDI portfolio visible to external actors and professionalise their services
related to RDI activities.
With the help of the call, a transparent system will be created, which, following a
“one-stop-shop” method, will make it easier to cooperate with local partners and
companies to find valuable knowledge or RDI capacity among university cooperation
opportunities. In this way, the university can become the main service provider of the
innovation space, thus helping SMEs in a wide range of innovation activities. This
system will also make it possible to further strengthen trust by enabling higher
education institutions to present their services in a manner that is clear and
comprehensible to businesses.
In Table 3, we summarise the analysed measures and their most important
channels affecting regional RDI actors, the expected positive outcome, and the
possible bottlenecks and hindering factors.

48 Zoltán Birkner – Ádám Mészáros – István Szabó
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Table 3
Summary of policy interventions: channels, outcomes, and bottlenecks in Hungary
Intervention Most important channels Expected positive
outcome
Possible bottlenecks or
hindering factors
Thematic Excellence
Programme Capacity building
Mission-oriented,
problem-centric, visible
research topics
Starting RDI
cooperation
Raising company’s
awareness on RDI
topics of universities
Weak motivation of
companies involved in
university projects
Centres for Higher
Education and Industrial
Cooperation and
Competence Centres
Capacity building
Project-oriented,
formalized RDI projects
Well-established
cooperation
Mutual understanding
of common vision
Possibility of utilising
the results for other
companies
Lost interest
of parties after finishing
the project,
unsustainable
cooperation
Science and Innovation
Parks Bringing together
regional actors in a
physical sense
Research, technology,
and related
infrastructure
Innovation services
Transferred general and
specific knowledge
Institutionalised
relationships
Weak local company
basis
Weak motivation to
move into to Science
Park
Dual training system Education (practice-
oriented curricula)
Possibilities of further
cooperation
Skilled workforce
Enhanced level of
innovation at local
actors
Understanding of the
advantages of the dual
training system
Cooperative doctoral
programme
Education (practice-
oriented curricula)
Generated side-projects
Utilisation of research
infrastructures
Skilled researchers
Stronger RDI activities
of enterprises
Common RDI projects
Understanding of the
advantages of the dual
training system
Brain drain of
researchers
Regional Innovation
Platforms Matchmaking
Access to research
results of other
programs
Finding project partners
Utilising results of other
projects
Weak local company
basis
Low level of
involvement
University Innovation
Ecosystem Visibility of RDI
infrastructures and
innovation services
Access to RDI
infrastructure
Access to services
Low level of business
awareness
Conclusions
Significant and long-standing regional disparities in RDI in Hungary pose challenges
for innovation policy. Instead of fully compensating for regional differences, the
development of regional RDI ecosystem actors based on local conditions and
opportunities and the strengthening of regional innovation capacity is the goal of the
policy, in line with Hungary’s Smart Specialisation and RDI strategies. Hungary

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considers higher education institutions as actors that play a major role in the region,
have a strong knowledge production capacity, and can potentially be considered the
main players in the flow of knowledge, and as such, to be the key to an innovation
system.
According to the intervention logic of the newly introduced measures, project-
and system-level programs launched on a university basis can have an impact on local
actors (through several channels). The university’s knowledge base can be exploited,
local businesses can increase their competitiveness through access to technology and
RDI services, and their innovation performance can be improved.
The intervention logics of the launched initiatives are dissimilar; the Cooperative
Doctoral Program exerts its impact by expanding research capacities tailored to local
requirements and utilising scientific results. The Thematic Research Excellence
Program concentrates research at universities and provides an opportunity for
companies engaged in R&D to become involved. Competence Centres create project-
level infrastructure and organizational conditions for sustainable collaboration. The
University Innovation Ecosystem makes RDI capacities visible to the actors of the
innovation system so that local businesses looking for solutions to their innovation
problems know where one-stop-shop capacities are available. In addition, Regional
Innovation Platforms bring together local actors, which can be the first step in
developing deeper cooperation and relationships. Science and Innovation Parks
provide the highest level of cooperation: they create infrastructure for all potential
participants, providing a systemic solution for knowledge producers and local users
to develop sustainable cooperation.
As all the analysed programs and institutions are new measures (were introduced
in the last two years or are planned to be implemented from 2021), in this study we
could draw up only their intervention logic and the expected results which probably
will have an impact on the regional innovation ecosystem and thus on regional
disparities. The planned evaluation and monitoring of the programs and regular
revision of the RDI and Smart Specialisation strategies provide an opportunity to
analyse the impact of the implementation of these measures on regional RDI
capacities and disparities.
REFERENCES
BEDŐ, Z.–ERDŐS, K. (2021): Az egyetem-központú vállalkozói ökoszisztéma és
megvalósításának lehetőségei Magyarországon (The university-based
entrepreneurship ecosystem and its possibilities). In: VARGA, A. (ed.): Regionális
innováció, vállalkozás és gazdasági növekedés (Regional innovation, entrepreneurship and economic
growth) pp. 89–101., Pécsi Tudományegyetem Közgazdaságtudományi Kar, Pécs.
BEDŐ, Z.–TOLMAYER, A. (2021): Egyetem-központú vállalkozói ökoszisztémák
státuszfelmérése (Status of university-based entrepreneurial ecosystems). In:
VARGA, A. (ed.): Regionális innováció, vállalkozás és gazdasági növekedés (Regional

50 Zoltán Birkner – Ádám Mészáros – István Szabó
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
innovation, entrepreneurship and economic growth) pp. 224–237., Pécsi Tudományegyetem
Közgazdaságtudományi Kar, Pécs.
BENNY, W. K. N.–APPEL-MEULENBROEK, R.–CLOODT, M.– ARENTZE, T. (2021): Perceptual
measures of science parks: Tenant firms’ associations between science park
attributes and benefits Technological Forecasting and Social Change 163: 120408
https://doi.org/10.1016/j.techfore.2020.120408
BERGHAEUSER, H.–HOELSCHER, M. (2020): Reinventing the third mission of higher
education in Germany: political frameworks and universities’ reactions Tertiary
Education Management 26: 57–76. https://doi.org/10.1007/s11233-019-09030-3
BÓDIS, J. (2021): Egyetem működtetése, összefoglalás a különböző típusokról, lehetőségek,
kihívások (Operation of the University, Summary of the Different Types,
Opportunities, Challenges) Magyar Tudomány 82 (11): 1502–1508.
https://doi.org/10.1556/2065.182.2021.11.9
BROEKEL, T.–BOSCHMA, R. (2016): The cognitive and geographical structure of knowledge
links and how they influence firms’ innovation performance Regional Statistics 6 (2):
3–26. https://doi.org/10.15196/RS06201
BUDAI, B. B.–TÓZSA, I. (2020): Regional inequalities in front-office services. Focus shift in e
government front offices and their regional projections in Hungary Regional
Statistics 10 (2): 206–227. https://doi.org/10.15196/RS100212
CAMAGNI, R.–CAPELLO, R.–CERISOLA S.FRATESI, U. (2020): Fighting Gravity: Institutional
Changes and Regional Disparities in the EU Economic Geography 96 (2): 108–136.
https://doi.org/10.1080/00130095.2020.1717943
CARAYANNIS, E. G.–CAMPBELL, D. F. J. (2009): 'Mode 3' and 'Quadruple Helix': toward a 21st
century fractal innovation ecosystem International Journal of Technology Management
46 (3): 201–234. https://doi.org/10.1504/IJTM.2009.023374
CARAYANNIS, E. G.–BARTH, T. D.–CAMPBELL, D. F. J. (2012): The Quintuple Helix
innovation model: Global warming as a challenge and driver for innovation Journal
of Innovation and Entrepreneurship 1 (2): 1–12.
https://doi.org/10.1186/2192-5372-1-2
COOKE, P. (1992): Regional Innovation Systems: Competitive Regulation in the New Europe
Geoforum 23 (3): 365–382. https://doi.org/10.1016/0016-7185(92)90048-9
DAVEY, T.–MEERMAN, A.–MUROS, V. G.–ORAZBAYEVA, B.–BAAKEN, T. (2018): The state of
university-business cooperation in Europe European Commission, Publications Office
of the European Union, Luxembourg.
DEÁK, C.–SZABÓ, I. (2016): Assessing cooperation between industry and research
infrastructure in Hungary Technology Innovation Management Review 6 (7): 13–20.
https://doi.org/10.22215/timreview/1001
DEMETER, G. (2020): Estimating regional inequalities in the Carpathian Basin – Historical
origins and recent outcomes (1880–2010) Regional Statistics 10 (1): 23–59.
https://doi.org/10.15196/RS100105
EDLER, J.–FAGERBERG, J. (2017): Innovation policy: what, why, how Oxford Review of Economic
Policy 33 (1): 2–23. https://doi.org/10.1093/oxrep/grx001
EGRI, Z.–TÁNCZOS, T. (2018): The spatial peculiarities of economic and social convergence
in Central and Eastern Europe Regional Statistics 8 (1): 49–77.
https://doi.org/10.15196/RS080108
EHLERS, U-D.–SCHENKEL, S.–TRATZMILLER, J. (2019): A new framework for professional
higher education: Creating synergie between theory and practice. The case of

Handling regional research, development and innovation (RDI) disparities
in Hungary, 2014–2021: New measures of university-based innovation ecosystem 51
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
Baden-Wuerttemberg Cooperative State University. In: International association of
sustainable globalization: Proceedings of international association of sustainable Globalization
2nd international conference, Kerala, India, 10–13 January, 2019
ERDŐS, K. (2019): Egyetemi vállalkozások Magyarországon – újragondolva? Közgazdasági
Szemle 66 (3): 305–329. http://dx.doi.org/10.18414/KSZ.2019.3.305
ETZKOWITZ, H.–LEYDESDORFF, L. (2000): The dynamics of innovation: From national systems and
mode 2 to a Triple Helix of University-Industry-Government Relations Introduction to the
special „Triple Helix” issue of Research Policy 29 (2): 109–123.
https://doi.org/10.1016/S0048-7333(99)00055-4
FAGERBERG, J.–SAPPRASERT, K. (2011): National innovation systems: The emergence of a
new approach Science and Public Policy 38 (9): 669–679.
https://doi.org/10.3152/030234211X13070021633369
FORAY, D. (2014): From smart specialisation to smart specialisation policy European Journal of
Innovation Management 17 (4): 492–507.
https://doi.org/10.1108/EJIM-09-2014-0096
FORAY, D. (2016): On the policy space of smart specialization strategies European Planning
Studies 24 (8): 1428–1437. https://doi.org/10.1080/09654313.2016.1176126
FRONDIZI, R.– FANTAUZZI, C.– COLASANTI, N.– FIORANI, G. (2019): The evaluation of
universities’ third mission and intellectual capital: Theoretical analysis and
application to Italy Sustainability 11: 3455. https://doi.org/10.3390/su11123455
GRASSELLI, N. (2006): A regionáis innováció hálózata – észak-alföldi helyzetkép (The network
of regional innovation – landscape of the Northern Great Plains) Területi Statisztika
46 (1): 264–273.
HALPERN, L.–MURAKÖZY, B. (2012): Innovation, productivity and exports: The case of
Hungary Economics of Innovation and New Technology 21 (2): 151–173.
https://doi.org/10.1080/10438599.2011.561995
ITM (2021a): Nemzeti Kutatási, Fejlesztési és Innovációs Stratégia 2021–2030 (National Research,
Development and Innovation Strategy 2021–2030) Ministry for Innovation and
Technology. Accepted by 1456/2021. (VII. 13.) Gov. decree.
ITM (2021b): Nemzeti Intelligens Szakosodási Stratégia 2021–2037. (National Smart
Specialization Strategy 2021–2027). Ministry for Innovation and Technology.
Accepted by 1428/2021. (VII. 2.) Gov. decree.
LUNDVALL, B.-Å. (2007): National Innovation Systems – Analytical concept and development
tool Industry and Innovation 14 (1): 95–119.
https://doi.org/10.1080/13662710601130863
MANSURY, A. M.–LOVE, J. H. (2008): Innovation, productivity and growth in US business
services: A firm-level analysis Technovation 28 (1–2): 52–62.
http://dx.doi.org/10.1016/j.technovation.2007.06.002
MCCANN, P.–ORTEGA-ARGILÉS, R. (2016): Smart specialisation, entrepreneurship and SMEs:
Iissues and challenges for a results-oriented EU regional policy. Small Business
Economics 46 (4): 537–552. https://doi.org/10.1007/s11187-016-9707-z
MCCARTHY, I. P. (2018): A typology of university research park strategies: What parks do and
why it matters Journal of Engineering and Technology Management 47 (January–March):
110–122. https://doi.org/10.1016/j.jengtecman.2018.01.004
SEBESTYÉN, T.–BRAUN, E.–ILOSKICS, Z.–VARGA, A. (2021): Spatial and institutional
dimensions of research collaboration: a multidimensional profiling of European
regions Regional Statistics 11 (2): 1–29. https://doi.org/10.15196/RS110203

52 Zoltán Birkner – Ádám Mészáros – István Szabó
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
SZABÓ, M. (2017): Spatial distribution of the top 500 companies on regional and county levels
in Hungary – a repeated analysis. Regional Statistics 7 (2): 148–170.
https://doi.org/10.15196/RS070208
TÉSITS, R.–ZSIGMOND, T.–ALPEK, L.–HOVÁNYI, G. (2021): The role of endogenous capital
factors in the territorial development of the Sellye District in Hungary. Regional
Statistics 11 (1): 58–77. https://doi.org/10.15196/RS110103
TVRDOŇ, M.–SKOKAN, K. (2011): Regional disparities and the ways of their measurement:
the case of the Visegrad countries Technological and Economic Development of Economy
17 (3): 501–518. https://doi.org/10.3846/20294913.2011.603485
VALIENTE, O.–SCANDURRA, R. (2017): Challenges to the implementation of dual
apprenticenships in OECD countries: A literature review. In: PILZ, M. (ed.):
Vocational Education and Training in Times of Economic Crisis. Technical and Vocational
Education and Training: Issues, Concerns and Prospects, vol. 24. Springer, Cham.
https://doi.org/10.1007/978-3-319-47856-2_3
VARGA, A. (2021): Az innováció, a vállalkozás és a gazdasági növekedés térbelisége (Spatial aspects
of innovation, entrepreneuship and economic growth. In: VARGA, A. (ed.): Regionális
innováció, vállalkozás és gazdasági növekedés (Regional innovation, entrepreneurship and economic
growth) pp. 9–21., Pécsi Tudományegyetem Közgazdaságtudományi Kar, Pécs.
INTERNET SOURCES
DANISH AGENCY FOR SCIENCE, TECHNOLOGY AND INNOVATION (2012): Introduction to the
Danish Industrial PhD Programme – structure, implementation and effects.
https://ufm.dk/en/publications/2012/introduction-to-the-danish-industrial-
phd-programme-structure-implementation-and-effects
(downloaded: 09 December 2021)
DHBW (2021): From BA to the Baden-Wuerttemberg Cooperative State University Duale Hochschule
Baden-Württemberg. https://www.dhbw.de/english/dhbw/about-us/history
(downloaded: 09 December 2021)
DINTEREN, J. (2021): Success factors of science parks re-examined Innovation Area Development
Partnership (IADP)
https://iadp.co/2021/03/15/success-factors-of-science-parks-re-examined/
(downloaded: 07 December 2021)
EDQUIST, C. (ed.) (2005): Systems of innovation: technologies, institutions and organizations,
Routledge.
https://charlesedquist.files.wordpress.com/2015/06/science-technology-and-
the-international-political-economy-series-charles-edquist-systems-of-
innovation_-technologies-institutions-and-organizations-routledge-1997.pdf
(downloaded: 10 March 2021)
ERNST&YOUNG (2020): A 2014–2020-as KFI-támogatások értékelése. Értékelési jelentés, 2020.
március 25. https://www.palyazat.gov.hu/a-2014-2020-as-kfi-tmogatsok-rtkelse#
(downloaded: 07 December 2021)
EUROPEAN COMMISSION (2012a): Guide to research and innovation strategies for smart specialisations
(RIS 3) Publications Office of the European Union, Luxembourg.
https://ec.europa.eu/regional_policy/sources/docgener/presenta/smart_special
isation/smart_ris3_2012.pdf (downloaded: 10 March 2021)

Handling regional research, development and innovation (RDI) disparities
in Hungary, 2014–2021: New measures of university-based innovation ecosystem 53
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
EUROPEAN COMMISSION (2012b): National/regional innovation strategy for smart specialization (S3).
https://ec.europa.eu/regional_policy/sources/docgener/informat/2014/smart_
specialisation_en.pdf (downloaded: 05 February 2021)
EUROPEAN COMMISSION (2020): Country report Hungary 2020 Brussels, 26.2.2020
https://eur-lex.europa.eu/legal-
content/EN/TXT/PDF/?uri=CELEX:52020SC0516&from=EN
(downloaded: 29 June 2021)
EUROPEAN COMMISSION (2021a): European and Regional Innovation Scoreboards 2021
https://ec.europa.eu/research-and-innovation/en/statistics/performance-
indicators/european-innovation-scoreboard/eis
(downloaded: 25 June 2021)
EUROPEAN COMMISSION (2021b): Horizon Europe, the EU research and innovation programme
(2021–27). https://op.europa.eu/en/web/eu-law-and-publications/publication-
detail/-/publication/93de16a0-821d-11eb-9ac9-01aa75ed71a1
(downloaded: 07 July 2021)
EUROPEAN PARLIAMENT AND EUROPEAN COUNCIL (2021): Regulation (EU) 2021/1060 of the
European Parliament and of the Council of 24 June 2021 laying down common provisions on the
European Regional Development Fund, the European Social Fund Plus, the Cohesion Fund, the
Just Transition Fund and the European Maritime, Fisheries and Aquaculture Fund and financial
rules for those and for the Asylum, Migration and Integration Fund, the Internal Security Fund
and the Instrument for Financial Support for Border Management and Visa Policy
https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32021R1060
(downloaded: 01 July 2022)
EUROPEAN UNION (2013): Regulation (EU) No 1303/2013 of the European Parliament and of the
Council of 17 December 2013
https://eur-lex.europa.eu/legal-
content/HU/TXT/HTML/?uri=CELEX:32013R1303&from=hu
(downloaded: 08 February 2021)
FORAY, D.–DAVID, P. A.–HALL, B. H. (2009): Smart specialisation: the concept. In: European
Commission: Knoweldge for Growth: Prospects for Science, Technology and Innovation pp. 25–
30., Publications Office of the European Union, Luxembourg.
https://ec.europa.eu/invest-inresearch/pdf/download_en/selected_papers_en.pdf
(downloaded: 08 February 2021)
FORAY, D.–MORGAN, K. – RADOSEVIC, S. (2018): The role of smart specialization in the EU research
and innovation policy landscape European Commission.
https://ec.europa.eu/regional_policy/sources/docgener/brochure/smart/role_s
martspecialisation_ri.pdf (downloaded: 08 February 2021)
HCSO – HUNGARIAN CENTRAL STATISTICAL OFFICE (2020a): Kutatás-fejlesztés, 2019−,
Innováció, 2016–2018 (Research and development, 2019, Innovation, 2016–2019)
Hungarian Central Statistical Office.
https://www.ksh.hu/docs/hun/xftp/idoszaki/tudkut/2019/index.html#tovbbi
adatokinformcik (downloaded: 18 October 2021)
HOLLANDERS, H. (2009): European Innovation Scoreboard (EIS): Evolution and Lessons Learnt.
https://www.oecd.org/dev/americas/42468972.pdf (downloaded: 25 May 2021)

54 Zoltán Birkner – Ádám Mészáros – István Szabó
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
KATO, S.–GALÁN-MUROS, V.–WEKO, T. (2019): The emergence of alternative credentials.
OECD Education Working Papers No. 216.
https://www.oecd-ilibrary.org/docserver/b741f39e-
en.pdf?expires=1625497653&id=id&accname=guest&checksum=88797BEF58
B291A0450ED7BEAF1A4138 (downloaded: 20 June 2021)
KUHN, J. M. (2011): Analysis of the Industrial PhD Program. CEBR – Centre for Economic and
Business Research. Published by The Danish Agency for Science, Technology and
Innovation.
https://innovationsfonden.dk/sites/default/files/2018-11/analysis-of-the-
industrial-phd-programme.pdf (downloaded: 07 December 2021)
LUKOVICS, M. (2009): Measuring Regional Disparities on Competitiveness Basis. In:
BAJMÓCY, Z.–LENGYEL, I. (eds.): Regional Competitiveness, Innovation and Environment
pp. 39–53., JATEPress, Szeged.
http://acta.bibl.u-szeged.hu/36246/1/gtk_2009_en_039-053.pdf
(downloaded: 10 February 2021)
MAZZUCATO, M. (2018): Mission-Oriented Research & Innovation in the European Union. A
problem-solving approach to fuel innovation-led growth. European Commission,
Brusseles.https://ec.europa.eu/info/sites/default/files/mazzucato_report_2018.
pdf (downloaded: 12 July 2021)
NKTH – NEMZETI KUTATÁSI ÉS TECHNOLÓGIAI HIVATAL (2004): Regionális Egyetemi
Tudásközpontok. Tájékoztató. (Regional University Knowledge Centres). National
Research and Technological Office.
https://nkfih.gov.hu/palyazoknak/regionalis-egyetemi/csomag
(downloaded: 08 December 2021)
NRDIO – NATIONAL RESEARCH, DEVELOPMENT AND INNOVATION OFFICE (2019): Centres
for Higher Education and Industrial Cooperation, Hungary: Case study contribution to the
OECD TIP Knowledge Transfer and Policies project.
https://stip.oecd.org/assets/TKKT/CaseStudies/10.pdf
(downloaded: 15 December 2020)
OECD (2007): National Innovation Systems. https://www.oecd.org/science/inno/2101733.pdf
(downloaded: 15 January 2021)
ORI, M. (2013): The rise of industrial PhDs. University World News.
https://www.universityworldnews.com/post.php?story=20131210130327534
(downloaded: 07 December 2021)
SPIEZIA, V. (2003): Measuring regional economies. OECD Statistics Brief, No. 6.
https://www.oecd.org/sdd/15918996.pdf (downloaded: 20 March 2021)
VILLAVERDE, J.–MAZA, A. (2009): Measurement of regional economic disparities. UNU-CRIS
Working Papers W-2009/12
https://cris.unu.edu/sites/cris.unu.edu/files/W-2009-12.pdf
(downloaded: 20 March 2021)
WASP (2019): WASP Industrial PhD Report 2019. Wallenberg AI, Autonomous Systems and
Software Program.
https://wasp-sweden.org/wp
content/uploads/2019/12/IndustrialPhD_Final_print.pdf
(downloaded: 07 December 2021)

Handling regional research, development and innovation (RDI) disparities
in Hungary, 2014–2021: New measures of university-based innovation ecosystem 55
Regional Statistics, Vol. 12. No. 4. 2022: 27–55; DOI: 10.15196/RS120402
WEBSITES/DATABASES
[1] EC (2009): Regional Innovation Scoreboard (RIS) 2009. European Commission.
https://op.europa.eu/en/publication-detail/-/publication/438811f6-bc27-4049-
9872-ad76db87f01e (downloaded: 10 June 2021)
[2] EC (2012): Regional Innovation Scoreboard (RIS) 2012. European Commission.
https://op.europa.eu/en/publication-detail/-/publication/aaff75f0-8d26-4503-
96a4-a61a7906d133 (downloaded: 10 June 2021)
[3] EC (2014): Regional Innovation Scoreboard (RIS) 2014. European Commission.
https://op.europa.eu/en/publication-detail/-/publication/69a64699-18d7-
40b9-8f92-1db3226cd2ec (downloaded: 10 June 2021)
[4] EC (2017): Regional Innovation Scoreboard (RIS) 2017. European Commission.
https://op.europa.eu/en/publication-detail/-/publication/ce38bc9d-5562-11e7-
a5ca-01aa75ed71a1/language-en (downloaded: 10 June 2021)
[5] EC (2019): Regional Innovation Scoreboard (RIS) 2019. European Commission.
https://ec.europa.eu/docsroom/documents/37783/attachments/1/translations
/en/renditions/native (downloaded: 10 June 2021)
[6] EC (2021): Regional Innovation Scoreboard (RIS) 2021. European Commission.
https://op.europa.eu/hu/publication-detail/-/publication/b76f4287-0b94-11ec-
adb1-01aa75ed71a1 (downloaded: 10 June 2021)
[7] EUROSTAT (2021a): GERD by sector of performance and source of funds [rd_e_gerdfund].
https://ec.europa.eu/eurostat/data/database (downloaded: 08 December 2021)
[8] EUROSTAT (2021b): Community Innovation Survey (CIS)
https://ec.europa.eu/eurostat/data/database (downloaded: 30 June 2021)
[9] HCSO – HUNGARIAN CENTRAL STATISTICAL OFFICE (2020b): A kutatás-fejlesztési
ráfordítások (falakon belüli) pénzügyi forrásai szektoronként (Sources of research and
development expenditures (in-house)by sector).
https://www.ksh.hu/stadat_files/tte/hu/tte0011.html
(downloaded: 08 December 2021)
[10] STIP COMPASS (2021): Database of OECD. https://stip.oecd.org/stip/
(downloaded: 07 December 2021)