Regional smart specialization in Ukraine: JRC methodology applicability

Abstract

Regional development is related to the effective management of disruptive industries on the local level. In the European Union, the innovation regional development policy is based on a smart specialization strategy, which neighboring countries try to apply as well. In their regional strategies, they notice the goals which are designed within the Joint Research Center methodology. It allows revealing the most efficient industries in the region, leading to a new level of regional competence on the global level. The study aims to identify smart specialization priorities based on JRC methodology in certain Ukrainian regions and assess its applicability in emerging markets (Ukrainian case) and develop the set of recommendations considering the specificity of the national economy. The methodology is based on the static and dynamic analysis of economic (the indicators of the growth of average salaries and the number of employees indicators are calculated) and innovation (the indicators of productive, process, organizational, and marketing innovations are analyzed) potential of the region, which is examined in the article. It is revealed that the JRC methodology in identifying the smart specialization priorities has limited application in Ukraine. The restrictions related to the lack of data on innovations and other economic indicators. The analysis of certain regions shows what industries should be recommended as the priorities of smart specialization. However, discussions of the calculated results with the key stakeholders have differences which are not acceptable in the regional innovation policy development. As a result, the experts’ opinions are recommended to consider the priorities of different regions in Ukraine and other developing countries, which are on the path of smart specialization during stakeholders’ communication sessions.

Full text

“Regional smart specialization in Ukraine: JRC methodology applicability”
AUTH ORS
Alla Ivashchenko https://orcid.org/0000-0002-4599-7137
http://www.researcherid.com/rid/J-1444-2018
Anna Kornyliuk https://orcid.org/0000-0001-8713-0681
Yevheniia Polishchuk https://orcid.org/0000-0002-6133-910X
http://www.researcherid.com/rid/J-5444-2018
Tetiana Romanchenko https://orcid.org/0000-0002-1663-1945
Iryna Reshetnikova https://orcid.org/0000-0003-1445-4233
http://www.researcherid.com/rid/E-2646-2018
ARTICLE INFO
Alla Ivashchenko, Anna Kornyliuk, Yevheniia Polishchuk, Tetiana Romanchenko
and Iryna Reshetnikova (2020). Regional smart specialization in Ukraine: JRC
methodology applicability. Problems and Perspectives in Management, 18(4),
247-263. doi:10.21511/ppm.18(4).2020.21
DOI http://dx.doi.org/10.21511/ppm.18(4).2020.21
RELEASED ON Friday, 11 December 2020
RECE IVED ON Wednesday, 26 August 2020
ACCEPTED ON Wednesday, 18 November 2020
LICENSE
This work is licensed under a Creative Commons Attribution 4.0 International
License
JOURNAL "Problems and Perspectives in Management"
ISSN PRINT 1727-7051
ISSN ONLINE 1810-5467
PUBLISHER LLC “Consulting Publishing Company “Business Perspectives”
FOUNDER LLC “Consulting Publishing Company “Business Perspectives”
NUMBER OF REFERENCES
40
NUMBER OF FIGURES
1
NUMBER OF TABLES
6
© The author(s) 2021. This publication is an open access article.
businessperspectives.org

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Problems and Perspectives in Management, Volume 18, Issue 4, 2020
http://dx.doi.org/10.21511/ppm.18(4).2020.21
Abstract
Regional development is related to the eective management of disruptive industries
on the local level. In the European Union, the innovation regional development policy
is based on a smart specialization strategy, which neighboring countries try to apply
as well. In their regional strategies, they notice the goals which are designed within the
Joint Research Center methodology. It allows revealing the most ecient industries in
the region, leading to a new level of regional competence on the global level. e study
aims to identify smart specialization priorities based on JRC methodology in certain
Ukrainian regions and assess its applicability in emerging markets (Ukrainian case)
and develop the set of recommendations considering the specicity of the national
economy.
e methodology is based on the static and dynamic analysis of economic (the indi-
cators of the growth of average salaries and the number of employees indicators are
calculated) and innovation (the indicators of productive, process, organizational, and
marketing innovations are analyzed) potential of the region, which is examined in the
article. It is revealed that the JRC methodology in identifying the smart specialization
priorities has limited application in Ukraine. e restrictions related to the lack of
data on innovations and other economic indicators. e analysis of certain regions
shows what industries should be recommended as the priorities of smart specializa-
tion. However, discussions of the calculated results with the key stakeholders have dif-
ferences which are not acceptable in the regional innovation policy development. As
a result, the experts’ opinions are recommended to consider the priorities of dierent
regions in Ukraine and other developing countries, which are on the path of smart
specialization during stakeholders’ communication sessions.
Alla Ivashchenko (Ukraine), Anna Kornyliuk (Ukraine), Yevheniia Polishchuk (Ukraine),
Tetiana Romanchenko (Ukraine), Iryna Reshetnikova (Ukraine)
Regional smart
specialization in Ukraine:
JRC methodology
applicability
Received on: 26 of August, 2020
Accepted on: 18 of November, 2020
Published on: 11 of December, 2020
INTRODUCTION
Implementing the smart specialization approach in regional strategies
is considered a key factor in achieving a disruptive regional develop-
ment. Smart specialization allows the region to be competitive on a
global scale. e identication of the main priorities determining the
direction of regional development is extremely important. e region-
al innovation policies cannot support all the industries for further de-
velopment. us, the most perspective and the eective ones should
be identied for their point stimulation through R&D, direction of
nancial support, engaging private stakeholders in innovation entre-
preneurship, and other instruments. Accurate calculations based on
reliable statistics can support this process, and the results should be a
basis for certain regional strategy.
Moreover, the revealed smart specialization priorities play a crucial
role in building a network between dierent regions, even those which
© Alla Ivashchenko, Anna Kornyliuk,
Yevheniia Polishchuk,
Tetiana Romanchenko, Iryna
Reshetnikova, 2020
Alla Ivashchenko, Ph.D. in Economics,
Associate Professor, Corporate Finance
and Controlling Department, Kyiv
National Economic University named
aer Vadym Hetman, Ukraine.
Anna Kornyliuk, Ph.D. in Economics,
Associate Professor, Corporate Finance
and Controlling Department, Kyiv
National Economic University named
aer Vadym Hetman, Ukraine.
Yevheniia Polishchuk, Doctor of
Economics, Professor, Corporate
Finance and Controlling Department,
Kyiv National Economic University
named aer Vadym Hetman, Ukraine.
(Corresponding author)
Tetiana Romanchenko, Ph.D. Student,
Department of Regional Studies and
Tourism, Kyiv National Economic
University named aer Vadym Hetman,
Ukraine.
Iryna Reshetnikova, Doctor of
Economics, Professor, Marketing
Department, Kyiv National Economic
University named aer Vadym Hetman,
Ukraine.
is is an Open Access article,
distributed under the terms of the
Creative Commons Attribution 4.0
International license, which permits
unrestricted re-use, distribution, and
reproduction in any medium, provided
the original work is properly cited.
www.businessperspectives.org
LLC “P “Business Perspectives”
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Sumy, 40022, Ukraine
BUSINESS PERSPECTIVES
JEL Classification R11, R13, O14, O31, Q55
Keywords innovative industries, regional development, smart
specialization, global competence, economic potential,
innovation potential
Conict of interest statement:
Author(s) reported no conict of interest

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Problems and Perspectives in Management, Volume 18, Issue 4, 2020
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are placed far apart from each other and, at the same time, have much in common r to boost and stim-
ulate regarded regions development.
is study provides an in-depth analysis of the regional economic potential based on smart specializa-
tion and innovative industries’ eectiveness in the selected Ukrainian regions. Identifying the capacity
of innovative industries is certainly challenging the performance of regional managers in the current
economic situation.
e research seeks to address the following questions:
1) to apply the pure smart specialization methodology for selected Ukrainian regions to evaluate the
economic and innovation potential of the region;
2) to identify the weaknesses of the application of smart specialization methodology in emerging mar-
kets and to develop the ways of its customization.
Determining the relevant priorities for smart specialization is important in the domain of regional
development. e approbation of the methodology at the regional level in Ukraine was carried out to
analyze both its positive aspects and vulnerabilities. A non-evidence-based approach could lead to un-
reasonable nancial costs and irrational decisions regarding implementing the innovations and scien-
tic developments.
e resulting calculations by region revealed several industries that are expected to form the basis of
smart specialization. e general results of the study and recommendations can be used for other re-
gions that focus on smart specialization priorities determination in Ukraine.
1. LITERATURE REVIEW
A great deal of previous research into smart
specialization has focused on its importance in
forming regional development policies in the EU
and associated countries. Previous research has
demonstrated how the smart specialization ap-
proach unites different European regions on the
path to innovation development. As the smart
specialization approach has been launched in
certain countries, most papers are focused on
comparative analysis of regional development
indicators before and after smart specialization
implementation. Further, there is an analysis of
the most common approaches in smart special-
ization priorities assessment, which is the basis
for smart specialization steps.
Fedeli et al. (2019), Polido et al. (2019), and Kroll
(2019) explain what is smart specialization and
claim that it is based on a policy approach and
has certain difficulties in evaluation of its pri-
ority industries: social problems are not tak-
en into account, low integration of smart spe-
cialization aims into sustainable development
goals, need for strategy strengthening from the
regional to the national level. Smart speciali-
zation priorities assessment is also considered
through sustainable development on Poland’s
example (Murzyn, 2019), where the difficulties
with EU funding are explored if the chosen in-
dustries are not identified within the common
JRC methodology.
Santoalha (2019) draws attention that common-
ly recognized JRC methodology should be cus-
tomized. In his mind, the concept of Northern
regions is more efficient rather than Central and
Southern ones. Other scholars believe that the
JRC methodology for identifying smart special-
ization regional priorities should be considered
within the cluster approach to increase regional
competitiveness (Pronesti, 2018; Thissen et al.,
2013; Kholiavko et al., 2020). A similar idea is
described by Kopczynska and Ferreira (2018).
Several studies are focused on the industrial
component of smart specialization assessment;

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for instance, the need for smart specialization
priorities of water and wastewater economy is
explored (Machnik-Slomka, 2018). Another
example is raising the competitiveness of ru-
ral territories via smart specialization support
(Šipilova et al., 2017). It has been assumed that
diversification, transition, radical foundation,
and modernization are the main models of
smart specialization launching (Piirainen et al.,
2017).
Some scholars emphasize the importance of
stakeholders’ cooperation at each phase of
smart specialization as a part of methodology
(Gedminaitė-Raudonė et al., 2019; Giggord &
McKelvey, 2019; Höglund & Linton, 2018; Pirnau
et al., 2018; Lundström & Mäenpää, 2017).
Female entrepreneurs may also have the po-
tential to impact regional development if they
have the relationships with the diaspora in a
country of their business performance (Ratten
& Pollegrini, 2019), developing a wide network
abroad. Therefore, such non-economic factors
should also be taken into account while smart
specialization priorities are defined. A similar
position is in another paper where the impor-
tance of the network in the context of smart
specialization performance has highlighted the
assessment of economic impact (Varga et al.,
2018). Vittoria and Napolitano (2016) stress the
culture networks, which should be considered
within smart specialization priorities.
Foray (2016) relates smart specialization strate-
gies to development, industrial, and innovation
policies. Therefore, he emphasizes the impor-
tance of industrial and innovation indicators
should be considered in smart specialization
priorities assessment. The importance of inno-
vation policy concept and readiness to imple-
ment regional smart specialization in certain
countries is examined by Balland et al. (2018),
Gebhardt and Stanovnik (2016), Smolinski et al.
(2015), Prause (2014). Some scholars offer to ap-
ply a smart specialization approach to cities’ sus-
tainable development (Serbanica & Constantin,
2017; Lobanova et al., 2020). In our opinion,
cities’ smart specialization priorities should be
revised because, in pure view, it seems to have
many inaccuracies.
To improve the smart specialization assessment
priorities methodology, Szerb et al. (2020) offer
to launch the Regional Entrepreneurship and
Development Index (REDI) to optimize local
entrepreneurial discovery processes. One more
approach is developed by Varga et al. (2020),
where they adjusted traditional smart spe-
cialization methodology and designed GRM-
Hungary for evaluation of entrepreneurship and
innovation network policies.
A relatively small body of literature is concerned
with quantitative tools for identifying smart
specialization priorities. Few studies are devot-
ed to discussing issues of chosen smart speciali-
zation priorities due to traditional methodology,
which is contentious. At the same time, these
studies deserve attention.
For instance, the index-based method is also
used for exploring smart economic develop-
ment, using data from six Central and Eastern
countries (Dagilienė et al., 2020). Poponi et al.
(2020) offer to use the cascade system in a mul-
ti-stakeholder perspective in the policy devel-
opment cycle to prioritize brunches instead of
commonly recognized methodology. This ap-
proach is worth attention; however, there is an
issue related to EU regional policy and funding
innovative projects.
In conclusion, these studies show that almost all
countries that entered the smart specialization
have issues customizing the commonly recognized
methodology of the JRC. For instance, it is only
quantitative and does not include national specif-
ics (what is appropriate for one region that is not
always good for the other), international commu-
nication development, the part of rural territories
and level of its development, etc. erefore, there is
a need to reveal on this phase what problems can
face Ukrainian regional local authorities assessing
smart specialization of priorities in each region.
2. AIMS
us, the research aims to identify smart spe-
cialization priorities based on JRC methodology
and assess its applicability in emerging markets
(Ukrainian case).

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3. METHODOLOGY
AND DATA
3.1. Data
According to CEA, the research is based on eco-
nomic data published by the State Statistics
Committee annually at the three and four-digit
industry level1 for 2013–2017.
European Commission recommends using the JRC
methodology of detecting smart specialization pri-
orities (Navarro et al., 2014). Despite the critical as-
sessments of other scholars on the possibility of ful-
ly applying this methodology, it is considered nec-
essary to test the methodology, identifying priority
areas for the smart specialization of two regions
– Cherkasy and Ivano-Frankivsk regions. is will
allow us to either conrm or reject previous re-
searchers’ views on the problems of applying this
technique in individual developing countries.
To determine regional Smart Specialization strate-
gy (S3), the local coecients (LQ) method was used.
is approach identies each sector value in the re-
gional economy compared to the national one.
e calculation is based on two groups of indica-
tors: economic potential and innovation potential
in static and dynamic dimensions.
e description of the applied 3-step methodology
is given further (details are given in Appendix B).
Step 1. Determination of economic potential of
the sector based on static and dynamic analysis
techniques
Static analysis indicates the industry’s contri-
bution to regional development at a certain date
(2018), while dynamic analysis indicates industry
development potential (from 2014 to 2018).
For static analysis, such indicators were used:
• number of employees – shows the potential of
the workforce to ensure the development of a
particular industry (main criterion);
1 CEA – Code of Economic Activity.
• wages level – an indicator of the attractive-
ness of the industry for employees (auxiliary
criterion).
e industry’s potential for the implementation
of SMART specialization is determined by the
coecient of local specialization of each indus-
try (both for the regional and national levels) (see
Appendix B).
To determine the future development potential of
industries, a dynamic analysis of the following in-
dicators was used:
• change in the industry employment share
from 2013 to 2017 (year to year and for the
whole period concerning the region and the
national economy);
• change in the industry wages in the region
from 2013 to 2017 (year to year and for the
whole period concerning the region and the
national economy).
Step 2. Definition of innovation potential
To determine innovation potential, such indica-
tors were used:
• the share of companies that have introduced
product innovations;
• the share of companies that have introduced
process innovations;
• the share of companies that have introduced
organizational innovations;
• the share of companies that have introduced
marketing innovations.
e selection of industries by innovation potential
considers two criteria of specialization: for the re-
gion and the whole economy (see Appendix B for
details).
Industries that meet all the above criteria have
the innovative potential to be part of the region’s
smart specialization.

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Step 3. Industry selection and analysis according
to both types of potential
At the nal stage, the selection of industries with
economic and innovation potential was made. 4
industries were identied for the Ivano-Frankivsk
region, and 3 for the Cherkasy region.
4. RESULTS
4.1. Analysis of the applied approach
to smart specialization
identification in Ukraine
4.1.1. Concept of industry selection within the
smart specialization framework
e key principle of smart specialization identi-
cation is to select priority industries on a region-
al basis taking into account the level of economic
and innovation potential in the direct region.
Because regional policy in Ukraine is under the
reformation processes based on smart speciali-
zation framework implementation principles, the
importance of relevant industry selection in every
region via proper quantitative and qualitative
measures is the key issue on the mentioned stage
(Figure 1).
Figure 1. Approaches for priority industries’ selecon in regions within
the smart specializaon framework
Source: Formed by the authors based on Navarro et al. (2014), Iacobucci (2012), Gianelle et al.
(2016), Gulc (2015), Soltys and Kamrowska-Zaluska (2016).
Smart specialization – policy concept aimed at identification priority domains for regional innovation policy intervention
Policy principles Regional
benchmarking Policy approaches Policy methods
Principle 1 – Granularity (sectoral
prioritization regarding
innovative orientation)
Principle 2 – Entrepreneurial
discovery (identifying business
capabilities in terms of R&D and
innovation in some industry or
subsystem)
Principle 3 – Priorities relevant
for today will not be supported
forever
Principle 4 – Inclusivity of SS
strategy (every sector having up-
to-date project could be
presented in the strategy)
Principle 5 – Experimentality and
need to be evaluated (clear
criteria for further action
assessment due to the
experimental nature of SS)
Step 1 – identification of
regions for comparison
Step 2 – determination of
similarities in structural
performance, as well as
relative strengths and
weaknesses
Step 3 – specification of
key causes leading to
better regional
performance
Step 4 – identification of
key stakeholder groups
and policymakers for
activity coordination and
evaluation
The top-down approach is suitable for
designing SS strategy with leading
stakeholders' contribution even on the
final stage of priority areas'
identification. This selection process
should consider such indicators as
number of regional firms involved in
R&D and innovation and total number
of supporting people; number of
researcher centers and university
departments; number of R&D projects;
number of patents, etc.
The bottom-up approach requires
analysis of quantitative and qualitative
data gathered from the main
technological, industry, and innovative
areas identified in the selected region to
justify the selected priorities via a top-
down approach
Methods of
triangulation should be
applied by a mix of
quantitative and
qualitative ones,
namely analysis of
science and
technology, statistic
methods, competitive
selection, scenario
analysis, targeted
surveys,
questionnaires, in-
depth interviews,
SWOT analysis, desk
research, expert
assessments, public
consultations, etc.
SS strategy design with the involvement of all groups of stakeholders: business, research, academia, public authorities, and society
and their continuous collaboration for SS further implementation

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e proposed concept for priority industry selec-
tion (see Figure 1) holding the policy principles,
regional benchmarking features, policy approach-
es, and methods could be a useful tool for key
stakeholder groups on the stage of key industries’
identication in a particular region.
e approaches for priority industry selection
within smart specialization framework are pre-
sented in Figure 1, and at regional benchmarking,
Frankivsk and Cherkasy regions were chosen as
ones that have similarities in regional economic
and innovation potential using static and dynam-
ic indicators (it is described in more detail in sub-
section 3.1.2).
4.1.2. Regional benchmarking: cases of Ivano-
Frankivsk and Cherkasy regions
Tables A1 and A2 (see Appendix A) summarize
the results of assessing the Ivano-Frankivsk and
Cherkasy regions’ economic potential. e thresh-
old values can also be changed to select more or
fewer industries. e initial selection included 201
industries for the Ivano-Frankivsk region and 153
industries for the Cherkasy region (without tak-
ing into account the threshold value); 74 and 68
industries passed at least one of 4 selection criteria
in Ivano-Frankivsk and Cherkasy regions, respec-
tively (employment, average wages, change in em-
ployment, change in average wages).
Based on static analysis, 30 and 32 industries
were identied in Ivano-Frankivsk and Cherkasy
regions using data “the number of employees”,
which is 44.4% and 54.5% of total employment in
each region. Using data “average wages”, 30 and
29 industries were identied, representing 24.8%
and 47.8% of total employment in each region.
Combining the results of these two criteria has
been matched in 13 and 16 industries, accounting
18.6% and 47.8% of total employment in Ivano-
Frankivsk and Cherkasy regions, respectively.
e dynamic analysis revealed 27 and 31 indus-
tries using changes in the number of employees,
amounted to 17.1% and 35.5% of total employ-
ment in Ivano-Frankivsk and Cherkasy regions.
Using data on changes in the average wages, 22
2 https://drive.google.com/le/d/1xWp00PAq_ep7JVuPFcEM33FLjy_hSj1n/view
and 16 industries were identied, constituted
16.3% and 8.7% of total employment for each re-
gion. According to these two criteria, the combi-
nation of results has coincided in 5 and 8 indus-
tries, which is 3.2% and 4.1% of total employment
in Ivano-Frankivsk and Cherkasy regions, respec-
tively. Only 1 industry in each region passes both
static and dynamic thresholds, employing 2.2%
and 1% in Ivano-Frankivsk and Cherkasy regions
(more detailed information of selected industries
is shown here)2, which shows the dened indus-
tries for each category.
e combination of certain industries accounts for
21.8% and 49.5% of total employment in the Ivano-
Frankivsk and Cherkasy regions. Only 1 industry
was identied in selected regions, using both the
criteria of static and dynamic analysis:
• 49.4 freight road transport, provision of
transportation services (for Ivano-Frankivsk
region);
• 47.3 retail sale of automotive fuel in special-
ized stores (for Cherkasy region).
Tables A3 and A4 (see Appendix A) generalize the
innovative potential assessment results for Ivano-
Frankivsk and Cherkasy regions. In 2016, 23 and
18 industries had innovative potential in Ivano-
Frankivsk and Cherkasy regions, respectively, 27
and 10 industries had innovative potential in the
country’s total industry, 22 and 10 industries had
innovative potential according to both criteria in
each region. In 2018, 29 and 21 industries had in-
novative potential accordingly in Ivano-Frankivsk
and Cherkasy regions, 28 and 11 industries had
innovative potential in the country’s total indus-
try, and 26 and 11 industries had innovative po-
tential, according to two criteria in the selected
regions. Combining results of 2016 and 2018 for
Ivano-Frankivsk region, 13 industries have inno-
vative potential over these years, which is 9.1% of
total employment in the region and, at the same
time, Cherkasy region has 3 industries with in-
novative potential, amounted to 1.7% of the total
number of employees in the region.
Using the alternative, selecting industries that

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passed at least 3 of the 4 criteria in 2016 and 2018,
it was revealed 16 and 6 industries for Ivano-
Frankivsk and Cherkasy regions, accounting for
14% and 3.2 % of total employment in each region
(more detailed information about industries with
innovative potential is shown here)3.
Tables A5 and A6 (see Appendix A) summarize
the results of economic and innovation poten-
tial for CEA B-E sectors in columns 3 and 6.
Column 7 shows whether the sector has eco-
nomic and innovation potential, and column
8 shows the share of employment in this area.
The main performance indicators for each of
these industries are included in Tables A5 and
A6 (see Appendix A). The criterion by which the
industry passed the critical value is highlighted
in blue if it has passed all static or dynamic eco-
nomic evaluation criteria in bold. If the innova-
tion criterion has been passed, all evaluations
for this year are highlighted.
Finally, 4 sectors in CEA B-E in the Ivano-
Frankivsk region match both criteria of econom-
ic and innovation potential, which is 7.6% of total
employment in the region:
• Manufacture of other textiles (CEA 13.9) has
been identied based on economic potential
in terms of employment and wages. e in-
novative potential is explained mainly by the
relatively high share of product and process
innovations in 2016 and 2018.
• Manufacture of articles of wood, cork, straw,
and plaiting materials (CEA 16.2) was deter-
mined based on current strong specialization
and average wages per employee. e innova-
tive potential is mainly according to the rela-
tively high share of innovation in 2018.
• Production of basic chemical products, fer-
tilizers, and nitrogen compounds, plastics,
and synthetic rubber in primary forms (CEA
20.1) has been dened based on economic
growth potential. e innovative potential is
explained mainly by the relatively high share
of product and process innovations in 2016
and 2018.
3 https://drive.google.com/le/d/1xWp00PAq_ep7JVuPFcEM33FLjy_hSj1n/view
• Production of cement, lime, and gypsum mix-
tures (CEA 23.5) has been identied based on
economic potential. e innovative potential
is mainly due to the relatively high share of in-
novations in 2016 and 2018.
In addition to the mentioned 4 industries identi-
ed in the analysis, the following activities have a
high level of innovation:
• manufacture of beverages (CEA 11);
• manufacture of electric motors, generators,
transformers, electrical distribution, and con-
trol equipment (CEA 27.1);
• manufacture of games and toys (CEA 32.4).
A smart specialization for this region can also be
a symbiosis of the production of games and toys
(CEA 32.4) and the manufacture of wood, cork,
straw, and plant materials for weaving (CEA 16.2)
for the manufacture of environmentally friendly
wooden toys.
In the Cherkasy region, 3 sectors in CEA B-E
meets both terms of economic and innovation po-
tential, which is 1.5% of total employment in the
region:
• Manufacture of instruments and appliances
for measuring, testing, and navigation; watch-
es and clocks (CEA 26.5) has been determined
based on economic growth potential. e in-
novation potential is mainly due to the rela-
tively high share of innovations in 2016 and
2018.
• Manufacture of optical instruments and pho-
tographic equipment (CEA 26.7) was dened
according to current strong specialization
and average wages per employee. e innova-
tion potential is explained mainly by the high
share of product and process innovations,
marketing innovations in 2016, and product
and process innovations in 2018.
• Manufacture of other general-purpose ma-
chinery (CEA 28.2) has been specied based

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on economic potential in wages per employee.
e innovation potential is mainly due to the
relatively high share of product innovations
and marketing innovations in 2016 and 2018.
In addition to 3 industries identied in the anal-
ysis, the following activities have a high level of
innovation:
• processing and preserving of sh, crustaceans,
and mollusks (CEA 10.2);
• manufacture of beverages (CEA 11).
5. DISCUSSION
e applied methodology allowed quantifying
both the economic and innovation potential of
each regional sector of the economy. us, the de-
sign of a smart specialization strategy for a par-
ticular region is based on a data-driven approach.
Moreover, the use of unied methodology allows
comparing the study results of dierent Ukrainian
regions and increases the smart specialization ap-
proach’s eectiveness at the national level.
However, some specic features of the method-
ology should also be addressed. First, access to
data is dicult: the State Statistics Committee’s
information is not always up-to-date and com-
plete. e risk of incomplete information can be
mitigated by obtaining data provided by regional
stakeholders directly. Second, the methodology
does not take into account shadow employment
and income received by self-employed individu-
als. ird, the results of the analysis largely de-
pend on the threshold values of the local quo-
tient coecients. To eliminate the shortcomings
mentioned above in smart specialization strategy
design and the use of quantitative methods, it is
recommended to attract local experts and adjust
the methodology according to unique regional
features. Further research will focus on develop-
ing qualitative methodology (specically struc-
tured interviews and focus groups in particular
regions) to take into account all mentioned above
shortcomings.
CONCLUSION
e JRC methodology for Ukrainian regions (Ivano-Frankivsk and Cherkasy) indicates priority in-
dustries based on existing regional economic and innovation potential. As a result of assessing the
economic potential according to JRC methodology, only one industry meets the criteria of static and
dynamic analysis in both analyzed regions. For innovation analysis, indicators of 13 industries in the
Ivano-Frankivsk region and 3 industries in the Cherkasy region represent the innovation potential
over the analyzed period from 2016 to 2018. Combining the results of economic and innovation po-
tential assessment, 4 industries of Ivano-Frankivsk region and 3 industries of Cherkasy region meet
both criteria (amounted 7.6% and 1.5% of total employment in each region). e obtained results
of industry analysis could be included in regional development strategies in the context of smart
specialization.
However, the mentioned methodology is not purely applicable for emerging economies and Ukraine
in particular due to the high level of the shadow economy, information asymmetry among potential
stakeholders, and lack of transparent and reliable data. Insucient statistical data are an additional
reason for such a limited number of industries, because individual entrepreneurs are not included in
the statistical database according to JRC methodology and untransparent wages payments (e.g., en-
velop wages).
It should be emphasized that the presented methodology does not consider the concentration of chosen
industry companies in the regional economy, suggesting high levels of the economic and innovative po-
tential of the industry can be provided by only one large enterprise. Besides, the companies of the cho-
sen industry can be located in one particular area of the region. erefore, the industry input in smart
specialization development can be limited.

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http://dx.doi.org/10.21511/ppm.18(4).2020.21
Only quantitative analysis is not sucient for achieving sustainable regional development and improv-
ing the regional innovation ecosystem. However, it is reasonable to supplement the assessment by qual-
itative methods with stakeholders’ groups’ involvement exemplied as regional policymakers, business,
academia, and public society activists. e expert opinions gathering by conducting in-depth inter-
views, focus groups, online surveys, etc., among potential stakeholders, namely business representatives
of key regional industries, individual entrepreneurs, academic society, NGOs, and regional authori-
ties, could be considered the eective instrument for industry selection within the smart specialization.
us, regional experts’ involvement is essential in evaluating the industry capacity to become a part of
smart specialization, which could be performed within the next stage of assessment, based on qualita-
tive methods.
To sum up, the JRC methodology is a generalizing tool for regional authorities who are well aware of the
specics of regional development, but this methodology in Ukrainian regions should be applied only in
complex of quantitative and qualitative methods to select the industries, which contributed the most to
the regional ecosystem in terms of economic and innovation development.
AUTHOR CONTRIBUTIONS
Conceptualization: Yevheniia Polishchuk, Iryna Reshetnikova.
Data curation: Alla Ivashchenko, Anna Kornyliuk.
Formal analysis: Alla Ivashchenko, Anna Kornyliuk, Tetiana Romanchenko.
Funding acquisition: Yevheniia Polishchuk.
Investigation: Anna Kornyliuk.
Methodology: Anna Kornyliuk.
Project administration: Iryna Reshetnikova.
Resources: Yevheniia Polishchuk, Iryna Reshetnikova.
Soware: Iryna Reshetnikova.
Supervision: Alla Ivashchenko.
Validation: Alla Ivashchenko.
Visualization: Alla Ivashchenko.
Writing – original dra: Alla Ivashchenko, Anna Kornyliuk, Yevheniia Polishchuk, Tetiana
Romanchenko.
Writing – review & editing: Yevheniia Polishchuk, Tetiana Romanchenko.
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APPENDIX A
Table A1. Reecon of economic potenal: results of Ivano-Frankivsk region
Criterion Threshold value
Number of
selected
industries
Share of regional
employment
The inial number of industries included in the analysis – 201 –
Stac analysis – employment
Degree of specializaon 1.5 46 –
Crical mass 0.25% 67 –
Both criteria –30 44.4%
Stac analysis – average wages
Regarding the region 0.8 77 –
Regarding the aggregate industry 0.9 35 –
Both criteria –30 24.8%
Employment and average wages –13 18.6%
Dynamic analysis – change in employment
Regarding the region 3 out of 5 years 35 –
Regarding the aggregate industry 3 out of 5 years 53 –
Both criteria –27 17. 1%
Dynamic analysis – change in average wages
Regarding the region 3 out of 5 years 24 –
Regarding the aggregate industry 3 out of 5 years 42 –
Both criteria –22 16.3%
Change in employment and change in average wages – 5 3.2%
Stac and dynamic analysis – 1 2.2%
Table A2. Reecon of economic potenal: results of Cherkasy region
Criterion Threshold value
Number of
selected
industries
Share of regional
employment
The inial number of industries included in the analysis –153 –
Stac analysis – employment
Degree of specializaon 1.5 38 –
Crical mass 0.25% 66 –
Both criteria –32 54.6%
Stac analysis – average wages
Regarding the region 0.9 52 –
Regarding the aggregate industry 0.9 38 –
Both criteria –29 47. 8 %
Employment and average wages –16 41. 8%
Dynamic analysis – change in employment
Regarding the region 3 out of 5 year s 48 –
Regarding the aggregate industry 3 out of 5 year s 47 –
Both criteria –31 17. 1%
Dynamic analysis – change in average salary
Regarding the region 3 out of 5 year s 28 –
Regarding the aggregate industry 3 out of 5 year s 27 –
Both criteria –16 8.7 %
Change in employment and change in average wages – 8 4.1%
Stac and dynamic analysis – 1 1.0%

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Table A3. Reecon of innovaon potenal: results of Ivano-Frankivsk region
Criterion Threshold value
Number of
selected
industries
Share of
regional
employment
The inial number of industries included in the
analysis – 201 –
2016
Innovave potenal in the region LQ above 1.1 in 2 types of innovaons 23 –
Innovave potenal for aggregate industr y LQ above 1.1 in 2 types of innovaons 27 –
Both criteria 22 –
2018
Innovave potenal in the region LQ above 1.1 in 2 types of innovaons 29 –
Innovave potenal for aggregate industr y LQ above 1.1 in 2 types of innovaons 28 –
Both criteria 26 –
2016 and 2018 All criteria for 2016 and 2018 13 9.1%
2016 and 2018 – alternave At least 3 of 4 criteria in 2016 and 2018 16 14%
Table A4. Reecon of innovaon potenal: results of Cherkasy region
Criterion Threshold value
Number of
selected
industries
Share of
regional
employment
The inial number of industries included in the
analysis –153 –
2016
Innovave potenal in the region LQ above 1.1 in 2 types of innovaons 18 –
Innovave potenal for aggregate industr y LQ above 1.1 in 2 types of innovaons 10 –
Both criteria 10 –
2018
Innovave potenal in the region LQ above 1.1 in 2 types of innovaons 21 –
Innovave potenal for aggregate industr y LQ above 1.1 in 2 types of innovaons 11 –
Both criteria 11 –
2016 and 2018 All criteria for 2016 and 2018 31.7%
2016 and 2018 – alternave At least 3 of the 4 criteria in 2016 and 2018 63.2%

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Table A5. Main eciency indicators for industries with economic and innovaon potenal of Ivano-Frankivsk region
CEA Industry
Degree of specializaon, average for 2012–2018
Share of regional employment; average for
2012–2018
Salary per employee in the region, the average for
2012–2018
Salary per employee in industry in the country, the
average for 2012–2018
Change in employment in the region, 2012–2017
(posive for # years)
Change in employment in industry in the countr y,
2012–2017 (posive for # years)
Change in salary per employee in relaon to the
region, 2012–2017 (posive for # years)
Change in salary per employee for industry in the
country, 2012–2017 (posive for # years)
Innovaons, degree
of specializaon in
the region, 2016
Innovaons, degree
of specializaon
in industry in the
country, 2016
Innovaons, degree
of specializaon in
the eld, 2018
Innovaons, degree
of specializaon
in industry in the
country, 2018
Product innovaons
Processing innovaons
Organizaonal innovaons
Markeng innovaons
Product innovaons
Processing innovaons
Organizaonal innovaons
Markeng innovaons
Product innovaons
Processing innovaons
Organizaonal innovaons
Markeng innovaons
Product innovaons
Processing innovaons
Organizaonal innovaons
Markeng innovaons
13.9 Manufac ture of other
texles 2.98 6 0.6% 145.2 160.9 0
(1)
0
(0)
0
(1)
0
(1) 1.06 1.27 5.24 4.74 1.92 1.22 5.96 3.35 0.00 2.08 0.00 2. 32 0.00 2.19 0.0 0 1.98
16.2
Manufacture of
wood, cork, straw
and plaing materials
5.659 2.3% 123.8 119.6 13.5%
(3)
7.7 %
(2)
25.5%
(2)
15.8%
(2) 0.91 0.55 1.12 03.71 1.37 1.73 0.0 1.99 1.38 1.91 1.03 2.69 1.6 1.60 0.92
20.1
Manufacture of basic
chemical products,
ferlizers and
nitrogen compounds,
plascs and synthec
rubber in primary
forms
3.464 2.9% 1 55.7 92.0 –8.9%
(1)
9.7%
(2)
–102.7%
(2)
–32.1%
(2) 1.82 3.28 4.49 4.07 3.64 2.85 4.86 3.36 3.41 3.56 1.63 1.33 2.86 2.57 1. 26 1.26
23.5
Producon of
cement, lime and
gypsum mix tures
13.822 1.8% 184.9 90.4 3.6%
(2) 22.9% (4) 24.6% (4) 33.2% (4) 2.12 2.55 5.24 4.74 3.22 1.93 4.83 3.22 0.0 0.00 5.72 4.6 4 – 0.00 3.83 1.92

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Table A6. Main eciency indicators for industries with economic and innovaon potenal of Cherkasy region
CEA Industry
Degree of specializaon, average for 2012-2018
Share of regional employment; average for
2012-2018
Salary per employee in the region, the average
for 2012-2018
Salary per employee in industry in the country,
the average for 2012-2018
Change in employment in the region, 2012-2017
(posive for # years)
Change in employment in industry in the
country, 2012-2017 (posive for # years)
Change in salary per employee in relaon to the
region, 2012-2017 (posive for # years)
Change in salary per employee for industry in
the countr y, 2012-2017 (posive for # years)
Innovaons, degree of
specializaon in the
region, 2016
Innovaons, degree
of specializaon
in industry in the
country, 2016
Innovaons, degree
of specializaon in
the eld, 2018
Innovaons, degree
of specializaon
in industry in the
country, 2018
Product innovaons
Processing innovaons
Organizaonal innovaons
Markeng innovaons
Product innovaons
Processing innovaons
Organizaonal innovaons
Markeng innovaons
Product innovaons
Processing innovaons
Organizaonal innovaons
Markeng innovaons
Product innovaons
Processing innovaons
Organizaonal innovaons
Markeng innovaons
26.5
Manufacture of
instrument s and appliances
for measuring , tesng and
navigaon, watches and
clocks
2.592 0.7 % 94.6 70.9 35.7 %
(2) 39.7% (3)32.1% (3) – 68.1% (2) 7. 22 0.0 0 0.00 8.90 3.25 0.00 0.00 2.60 5.26 2.96 0.00 0.00 2.37 1.23 0.00 0.0 0
26.7
Manufacture of opcal
instrument s and
photographic equipment
14.34 8 0.7% 123.7 118.1 9.8%
(3) 30.7 % (2) 4.0% (2) –1 60 .8 %
(2) 10.83 8.42 0.00 13.36 2.17 3.25 – 3.25 14.03 7. 8 9 0.00 0.00 1 .47 1.22 0.00 0.00
28.2 Manufac ture of other
general-purpose machinery 0.305 0.1% 122.2 104.4 30.1%
(3) 42.0% (3) –19. 9%
(2) –18 .0 % (2) 5.41 0.00 0.00 8.90 3.33 0.00 0.00 4.67 4.21 0.0 0 0.00 3.56 3.57 0.00 0.0 0 1 .79

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http://dx.doi.org/10.21511/ppm.18(4).2020.21
APPENDIX B.
Methodology description of detection smart specialization priorities
Step 1. Determination of economic potential of
the sector based on static and dynamic analysis
techniques
1.1. Static analysis
e coecient of local specialization by the crite-
rion of labor is determined as follows:
( ) ( )
/ /,LQEi ei e Ei E=
where LQEi – local coecient of specialization of
the industry i at the regional level by the criterion
of labor, ei – number of industry i employees at the
regional level, e – total number of employees at the
region, Ei – number of industry i employees at the
national level, E – total number of employees at the
national level.
e threshold for LQEi is > 1.5.
If LQEi > 1, it means that at the level of a particular
region, the share of employees in the industry is
greater than in the country as a whole, which may
indicate the specialization of the region in a par-
ticular industry. If LQEi < 1 – the share of industry
i employees is less compared to the national level.
In this study, the threshold value for LQEi is > 1.5.
To exclude too small and, accordingly, not inuen-
tial at the regional level, industries, an additional
coecient of critical mass was calculated:
cmi = ei/e,
where cmi – indicator of the relative size of the in-
dustry i in the regional economy, ei – number of
employees in industry i on regional level, e – total
number of employees in the regional economy.
e threshold of the coecient is > 0.25.
As mentioned above, the wages level was used as
an auxiliary selection criterion, for which the fol-
lowing indicators were calculated:
• average wages in the industry at the regional
level (aw i);
• average wages in the region (aw);
• average wages in a particular industry at the
state level (awi).
e following thresholds were set for the selection
of industries:
1) the size of the average wages in the industry at
the regional level (awi) must be at least 80% of
the average wages in the region (awi);
2) the size of the average wages in the industry
at the regional level (awi) must be at least 90%
of the average wages in the industry at the na-
tional level (awi).
Accordingly, only those sectors that meet the
above criteria were selected for further analysis.
1.2. Dynamic analysis
To be selected through dynamic analysis indus-
tries must meet the following requirements.
Employment criterion:
1) the growth rate of employees’ share is posi-
tive for the whole region in 2018 compared to
2014 and the annual growth rate employees
in the region is positive for at least 3 out of
5 years;
2) the growth rate of employment in the industry
on the national level is positive and the annual
growth rate of employment in the he industry
on the national level is positive for at least 3
out of 5 years.
Wages criterion:
1) the growth rate of the average wages is positive
for the whole region in 2018 compared to 2014

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CEA1 and the annual growth rate in average
wages is positive for at least 3 out of 5 years;
2) the growth rate of average wages in the indus-
try on the national level is positive and the an-
nual growth rate of average wages in the in-
dustry on the national level is positive for at
least 3 out of 5 years.
Industries that meet all the above criteria are con-
sidered to have economic potential for the imple-
mentation of smart specialization.
Step 2. Definition of innovation potential
Specialization in relation to the region shows
the contribution of a particular industry to the in-
novative development of the region and is deter-
mined by the following formula:
LQIir = (%in _ xi)/(%in_ x),
where LQIir – local coecient of specialization of
the industry i at the regional level, %in_xi – the
share of x-type innovations in industry i, %in_x
– the share of type x innovations in the regional
economy,  – type of innovation.
1 CEA – Code of Economic Activity.
Specialization in relation to the country reects
the innovative potential of a particular industry in
the region in relation to the entire industry of the
country and is determined by the formula:
LQIic = (%in_xi)/(%IN_xi),
where LQIic – local coecient of specialization
of industry i in the economy, %in_xi – the share
of x-type innovations in industry i, %IN_xi – the
share of type x innovations in industry across the
country, x – type of innovation.
In order for an industry to be selected for further
study, the following thresholds were set:
3. coecients of specialization of the industry
both in relation to the region and the total in-
dustry must be higher than 1.1;
4. coecients must be above the threshold for
at least two types of innovation out of four
possible.
Step 3. Industry selection according to the
above mentioned criterions