The development of innovation activities is of great importance on the path to achieving
the goals of sustainable development. Success on this path is closely related to the
presence of comparable information on the development of innovation activities at
the regional level. The aim of the paper is to assess the development of innovation
activities in the regions of Ukraine and identify differences in results. The study is performed
using relative indicators for the assessment of the development of innovation
activities in the regions of Ukraine. The indicators were averaged and normalized. To
analyze how innovation activities change over time, the dynamic indices based on the
geometric mean of the growth rate of the relative indicators were used. The obtained
results have significant differences in the regions being assessed. Most regions have a
heterogeneous development of innovation activities. At the same time, they are at the
top and bottom of the rankings of the regions in different indicators of the development
of innovation activities. Only Cherkasy and Zaporizhzhia oblasts are at the top
of the rankings in at least 75% of indicators. However, in 2017‒2019, all indicators
improved in at least 29% of regions. In addition, 75% of indicators improved in at least
54% of regions. Therefore, over time, most regions progressed in the development of
innovation activities. Management decisions for the development of innovation activities
should be complex for all regions and implemented primarily in the regions where
there is no improvement over time.
STRATEGIES FOR THE INSTITUTIONALIZATION OF INNOVATION PRACTICES IN RUSSIAN RE...IAEME Publication
The methodology of managing the social/economic sphere in a region incorporates both general foundations and particular specific prospects for fostering innovation. The innovation trajectory in present-day Russia is aimed at enhancing quality and improving productivity, expanding the markets, boosting the population’s well-being, and opening up new vistas of opportunity. Innovation plays a key role in resolving many regional issues that, above all, are associated with social/economic life in society. Today, a major factor in fostering human capital and speeding up the pace of innovation-driven development is the use of new technology, which is crucial to creating a successful system of interaction between the government, the business sector, industry, and the scientific/technical sector. But innovation is hardly possible without employing foresight, analysis, and forecasting, as the nature of social/economic development in any region may undergo changes over time based on a focus on cultivating innovation potential. This paper examines the current situation in Volgograd Oblast with a view to bringing forward a set of recommendations on resolving the region’s existing problems.
Innovation for Inclusive Development Program Prospectus for 2011-2016iBoP Asia
This document outlines a program called Innovation for Inclusive Development (IID) that aims to study innovation in informal sectors in developing countries. The program goals are to understand how innovation in the informal sector can improve livelihoods and contribute to inclusive development. It will focus on the role of women and intermediaries between informal and formal sectors in activities like natural resources, services, and cultural industries. The intended outcomes are for universities in low- and middle-income countries to conduct research on innovation for inclusive development, for science granting councils to fund this research, and for governments to develop policies that encourage and support innovation for inclusive development.
Innovation Ecosystems - Practice vs. Prevailing PerceptionsYifat Turbiner
The document discusses factors that influence innovation ecosystems based on interviews with 25 Israeli innovation leaders. It finds that while the key factors identified in literature - government, academia, venture funding, culture and technology - also influence Israel's system, the relative importance of each factor differs from prevailing perceptions. Specifically, culture was seen as making a major contribution, while government and academia's impacts were viewed as more moderate. This discrepancy may be due to ecosystems evolving over time, changing each factor's nature of contribution.
Innovation Ecosystems - Practice vs. Prevailing PerceptionsYifat Turbiner
The document discusses factors that influence innovation ecosystems based on interviews with 25 Israeli innovation leaders. It finds that while the key factors identified in literature - government, academia, venture funding, culture and technology - also influence Israel's system, the relative importance of each factor differs from prevailing perceptions. Specifically, culture was seen as making a major contribution, while government and academia's impacts were viewed as more moderate. This discrepancy may be due to ecosystems evolving over time, changing each factor's nature of contribution.
The regulatory environment for businesses in Ukraine has been considered unfavorable and market unfriendly. Although various governments have made numerous efforts to improve it, many of these attempts have failed and increasing the quality of the regulatory environment in the country still remains on the agenda of the government. With this report we claim to review a set of measures undertaken in Ukraine after the Orange Revolution in the area of deregulation of business activity. The paper analyzes the effectiveness of actions undertaken in Ukraine in a general framework of successful regulatory policies implemented in other parts of the world. Based on this analysis we developed concrete public policy measures aiming to increase the quality of the regulatory environment in the country, which, in turn, should secure Ukraine’s further movement toward a real, functioning market economy.
Authored by: Ewa Balcerowicz, Oleg Ustenko
Published in 2006
This document discusses promoting innovative industries and technologies for a sustainable future in the Europe and NIS region. It finds that current production and consumption patterns in the region are incompatible with sustainability objectives and fail to capitalize on growing sustainable goods and services markets. There is an urgent need for the region to transition to a new industrial growth model that is resource efficient, low carbon, low pollution and improves living standards. However, significant investment and policy support is needed to overcome barriers inhibiting sustainable technologies and enable countries in the region to transition to green economies and industries.
This paper examines the motives behind foreign direct investment (FDI) in a group of four CIS countries (Ukraine, Moldova, Georgia and Kyrgyzstan) based on a survey of 120 enterprises. The results indicate that non-oil multi-national enterprises (MNEs) are predominantly oriented at serving local markets. Most MNEs in the CIS operate as 'isolated players', maintaining strong links to their parent companies, while minimally cooperating with local CIS firms. The surveyed firms secure the majority of supplies from international sources. For this reason, the possibility for spillovers arising from cooperation with foreign-owned firms in the CIS is rather low at this time. The lack of efficiency-seeking investment poses further concern regarding the nature of FDI in the region. The most significant problems identified in the daily operations of the surveyed foreign firms are: the volatility of the political and economic environment, the ambiguity of the legal system and the high levels of corruption.
Authored by: Malgorzata Jakubiak
Published in 2008
The document discusses innovation and regional economic development. It covers several topics:
1. Innovation is key to improving productivity and economic growth. It creates competitive advantage by reducing costs and introducing new products.
2. Successful innovation depends on regional factors like assets, networks, and economic culture that make up the regional innovation environment. Assets include skilled labor, infrastructure, research programs, and more.
3. Indigenous innovative activities like R&D spending help explain differences in regional growth. Knowledge-rich regions have continuous advantages over areas relying on external technology.
STRATEGIES FOR THE INSTITUTIONALIZATION OF INNOVATION PRACTICES IN RUSSIAN RE...IAEME Publication
The methodology of managing the social/economic sphere in a region incorporates both general foundations and particular specific prospects for fostering innovation. The innovation trajectory in present-day Russia is aimed at enhancing quality and improving productivity, expanding the markets, boosting the population’s well-being, and opening up new vistas of opportunity. Innovation plays a key role in resolving many regional issues that, above all, are associated with social/economic life in society. Today, a major factor in fostering human capital and speeding up the pace of innovation-driven development is the use of new technology, which is crucial to creating a successful system of interaction between the government, the business sector, industry, and the scientific/technical sector. But innovation is hardly possible without employing foresight, analysis, and forecasting, as the nature of social/economic development in any region may undergo changes over time based on a focus on cultivating innovation potential. This paper examines the current situation in Volgograd Oblast with a view to bringing forward a set of recommendations on resolving the region’s existing problems.
Innovation for Inclusive Development Program Prospectus for 2011-2016iBoP Asia
This document outlines a program called Innovation for Inclusive Development (IID) that aims to study innovation in informal sectors in developing countries. The program goals are to understand how innovation in the informal sector can improve livelihoods and contribute to inclusive development. It will focus on the role of women and intermediaries between informal and formal sectors in activities like natural resources, services, and cultural industries. The intended outcomes are for universities in low- and middle-income countries to conduct research on innovation for inclusive development, for science granting councils to fund this research, and for governments to develop policies that encourage and support innovation for inclusive development.
Innovation Ecosystems - Practice vs. Prevailing PerceptionsYifat Turbiner
The document discusses factors that influence innovation ecosystems based on interviews with 25 Israeli innovation leaders. It finds that while the key factors identified in literature - government, academia, venture funding, culture and technology - also influence Israel's system, the relative importance of each factor differs from prevailing perceptions. Specifically, culture was seen as making a major contribution, while government and academia's impacts were viewed as more moderate. This discrepancy may be due to ecosystems evolving over time, changing each factor's nature of contribution.
Innovation Ecosystems - Practice vs. Prevailing PerceptionsYifat Turbiner
The document discusses factors that influence innovation ecosystems based on interviews with 25 Israeli innovation leaders. It finds that while the key factors identified in literature - government, academia, venture funding, culture and technology - also influence Israel's system, the relative importance of each factor differs from prevailing perceptions. Specifically, culture was seen as making a major contribution, while government and academia's impacts were viewed as more moderate. This discrepancy may be due to ecosystems evolving over time, changing each factor's nature of contribution.
The regulatory environment for businesses in Ukraine has been considered unfavorable and market unfriendly. Although various governments have made numerous efforts to improve it, many of these attempts have failed and increasing the quality of the regulatory environment in the country still remains on the agenda of the government. With this report we claim to review a set of measures undertaken in Ukraine after the Orange Revolution in the area of deregulation of business activity. The paper analyzes the effectiveness of actions undertaken in Ukraine in a general framework of successful regulatory policies implemented in other parts of the world. Based on this analysis we developed concrete public policy measures aiming to increase the quality of the regulatory environment in the country, which, in turn, should secure Ukraine’s further movement toward a real, functioning market economy.
Authored by: Ewa Balcerowicz, Oleg Ustenko
Published in 2006
This document discusses promoting innovative industries and technologies for a sustainable future in the Europe and NIS region. It finds that current production and consumption patterns in the region are incompatible with sustainability objectives and fail to capitalize on growing sustainable goods and services markets. There is an urgent need for the region to transition to a new industrial growth model that is resource efficient, low carbon, low pollution and improves living standards. However, significant investment and policy support is needed to overcome barriers inhibiting sustainable technologies and enable countries in the region to transition to green economies and industries.
This paper examines the motives behind foreign direct investment (FDI) in a group of four CIS countries (Ukraine, Moldova, Georgia and Kyrgyzstan) based on a survey of 120 enterprises. The results indicate that non-oil multi-national enterprises (MNEs) are predominantly oriented at serving local markets. Most MNEs in the CIS operate as 'isolated players', maintaining strong links to their parent companies, while minimally cooperating with local CIS firms. The surveyed firms secure the majority of supplies from international sources. For this reason, the possibility for spillovers arising from cooperation with foreign-owned firms in the CIS is rather low at this time. The lack of efficiency-seeking investment poses further concern regarding the nature of FDI in the region. The most significant problems identified in the daily operations of the surveyed foreign firms are: the volatility of the political and economic environment, the ambiguity of the legal system and the high levels of corruption.
Authored by: Malgorzata Jakubiak
Published in 2008
The document discusses innovation and regional economic development. It covers several topics:
1. Innovation is key to improving productivity and economic growth. It creates competitive advantage by reducing costs and introducing new products.
2. Successful innovation depends on regional factors like assets, networks, and economic culture that make up the regional innovation environment. Assets include skilled labor, infrastructure, research programs, and more.
3. Indigenous innovative activities like R&D spending help explain differences in regional growth. Knowledge-rich regions have continuous advantages over areas relying on external technology.
The purpose of the paper is to develop a formal method for monitoring and assessment of the process of sustainable national development. In particular, we apply cluster analysis to develop procedures of formal assessment of development gaps, and show how they can be applied for monitoring processes of sustainable development (the method proposed could also be applied for inter-country comparison and assessment of the impact of technical cooperation projects and different internal and external shocks on national development). A formal method is described and implemented for the analysis of national development processes in the Kyrgyz Republic in the period 1992-2000. The study reveals that although in the second part of the nineties many development indicators in the country improved significantly, the development path observed in this period can hardly be classified as sustainable.
Authored by: Jacek Cukrowski, Julia Mironova
Published in 2002
Building, embedding and reshaping Global Value Chains through investment flow...OECD CFE
Presentation by Oliver Harman, Cities Economist at Cities that Work, Oxford University, UK at the seventh meeting of the Spatial productivity Lab of the OECD Trento Centre held on 20 February 2020.
More info http://oe.cd/SPL
В отчете, который презентовали 29 мая в инновационном парке UNIT.City, были изучены наиболее значимые научные разработки, динамика инновационной активности, интересы международных компаний в Украине, а также украинских изобретателей за рубежом за 2007-2017 года. Проект был реализован совместными усилиями Innolytics Group, SingularityU Kyiv Chapter, UNIT.City, в партнерстве с DLA Piper и Ukrinnovate.
This document provides an overview of the OECD project on Innovation for Inclusive Growth and its 2015 report. The project aims to examine how innovation can promote inclusive development. It involved experts from various countries and organizations. The 2015 report focuses on inclusive innovations that improve welfare for disadvantaged groups. It discusses policy approaches to support such innovations and ensure they reach scale. The document outlines challenges like informality, access to expertise and finance, and regulatory issues. It proposes policy responses like cross-government coordination, public-private partnerships, and financial support to foster cooperation across actors and address challenges.
The problem of managing the R&D sector sustainability in Russia is of particular
relevance in terms of scaling external and internal challenges faced by the country. Such
challenges require an even greater intensification of the efforts to solve the problems
accumulated in Russian economy and innovation system and associated with the state
transition to new technological way. A key criterion of R&D sector competitive
sustainability is the creation of practice-oriented and relevant results of intellectual
activity. Correlation analysis of innovative activity indicators in developed countries over
the 2007-2015 revealed a pattern of two-fold excess of export licenses over the amount of
public investment in science. This dependence is crucial in the study of competitive
sustainability within R&D sector. The method of assessing the efficiency of public
spending on R&D, including an analysis of the dynamics of the growth rate of
performance indicators and their financing in case of R&D completed in the period is
proposed as the main management tool of R&D sector competitive sustainability.
The objective of the PICK-ME (Policy Incentives for Creation of Knowledge – Methods and Evidence) research project is to provide theoretical and empirical perspectives on innovation which give a greater role to the demand-side aspect of innovation. The main question is how can policy make enterprises more willing to innovate? This task is fulfilled by identifying what we consider the central or most salient aspect of a demand-side innovation- driven economy, which is the small and entrepreneurial yet fast growing and innovative firm. We use the term “Gazelle” to signify this type of firm throughout the paper. The main concern of policy-makers should therefore be how to support Gazelle type of firms through various policies. The effectiveness of different policy instruments are considered. For example, venture capitalism is in the paper identified as an important modern institution that renders exactly the type of coordination necessary to bring about an innovation system more orientated towards the demand side. This is because experienced entrepreneurs with superior skills in terms of judging the marketability of new innovations step in as financiers. Other factor market bottlenecks on the skills side must be targeted through education policies that fosters centers of excellence. R&D incentives are also considered as a separate instrument but more a question for future research since there is no evidence available on R&D incentives as a Gazelle type of policy. Spatial policies to foster more innovation have been popular in the past. But we conclude that whereas the literature often finds that new knowledge is developed in communities of physically proximate firms, there is no overshadowing evidence showing that spatial policies in particular had any impact on generating more of the Gazelle type of firms.
Authored by: Itzhak Goldberg, Camilla Jensen
Published in 2014
The Effects of Engineering Education and Government Policy in Driving Innovation among Engineering Graduates in Nigeria by Olawale Oshokoya* in Advancements in Civil Engineering & Technology
The Department of Commerce has determined the economy in the creative industry into 14 (fourteen) sectors: (1) advertising
services, (2) architecture, (3) art and antiques market, (4) craft, (5) design, (6) fashion, (7) video, film and photography, (8)
interactive games (9) music, (10) performing arts, (11) publishing and printing, (12) computer and software services, (13)
television and radio, (14) research and development. Mapping of creative industrial areas is done so that the process of guidance
and distribution of assistance from local and central government and the management of the creative industries from each regi on
focused so that the regions have characteristics in producing the creative industry and can compete in the current era of
globalization. Creative industry in Indonesia began to mushroom in 2007, but many people still do not understand what sectors are
included into the creative industry and its development. With so many people's lack of understanding of the creative industry, it
makes its own homework for the government to socialize the creative industries so that the government seeks to map the creative
industries in Indonesia. Though the creative industry is able to contribute greatly to the Gross Domestic Product (GDP) of an area
and create jobs.
This study aims to further understand the characteristics of creative industries, as well as to identify the constraints and
opportunities faced by creative industry players in Indonesia. To identify and analyze the creative industries used 4 quadrant
statistical methods Location Quadrant (LQ) and Dynamic Location Quotient (DLQ). This study uses secondary data of large and
medium industry statistics as well as small and micro industries in 2010-2013 at Provincial level obtained from the Central Bureau
of Statistics (BPS).
The results of this mapping study will provide a general overview of the creative industries sector that has competitiveness in
every region or province in Indonesia.
Modern Approaches and Strategies for the Implementation of Innovative Process...ijtsrd
The article describes the modern approaches, strategies and the role of innovation in the development of innovation processes in industrial enterprises of the Republic of Uzbekistan. Dexkhanova Nargiza Sharifovna "Modern Approaches and Strategies for the Implementation of Innovative Processes in Industrial Enterprises on the Example of Uzbekistan" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Modern Trends in Scientific Research and Development, Case of Asia , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35882.pdf Paper Url :https://www.ijtsrd.com/economics/other/35882/modern-approaches-and-strategies-for-the-implementation-of-innovative-processes-in-industrial-enterprises-on-the-example-of-uzbekistan/dexkhanova-nargiza-sharifovna
A Study On Determinants Of Private Investment Activity A Case Study Of Debre...Joaquin Hamad
This document summarizes a study on the determinants of private investment activity in Debre Tabor Town, Ethiopia. The study used both quantitative and qualitative methods to identify factors that influence investment from the perspective of 103 private investors. These factors included expected returns, business confidence, interest rates, corruption, savings levels, infrastructure development, and political stability. The study found that these determinants significantly impacted private investment activity in the town. Recommendations were made to policymakers on how to address challenges and promote greater private investment.
Proposal for Baseline Study for BSNL in New Tehri DistrictMohit Rajput
This document provides a proposal for conducting a baseline study in villages in New Tehri district, Uttarakhand, India. The study aims to assess current levels of education, literacy, skills, employment, and use of renewable energy in the region. The proposal outlines a methodology using both quantitative and qualitative research methods, including secondary data analysis, surveys, interviews, and focus groups. It proposes assessing indicators like literacy rates, school enrollment, employment levels, and impact of previous initiatives. The study aims to evaluate current status and measure progress of planned interventions in education, skills training, women's empowerment, and renewable energy development. It provides details on sampling, timelines, deliverables, and a 24-month budget of 17
This document discusses a study that examines the impact of different types of innovation on sales growth in small and medium enterprises (SMEs) in Kosovo. The study collected data from 278 SMEs through surveys. It analyzes the data using logistic regression to test hypotheses about the relationship between innovation types (product/process, marketing, organizational) and firm growth, measured by sales. The findings indicate that marketing innovation is positively associated with sales growth, while new products introduced only within the firm are negatively associated with growth. Other innovation attributes showed no significant relationship. The study aims to provide insights for both theory and policy regarding SME development and innovation in Kosovo.
Socio-Economic Regional Disparities in National DevelopmentIRJET Journal
This document analyzes socio-economic regional disparities in India. It discusses the need to study disparities as government policies have increased the gap between rich and poor states. Four indicators are selected to identify spatial patterns of regional disparities: population, population density, decadal growth rate, and literacy rate. The methodology involves analyzing secondary data on these and other socio-economic indicators for different districts over time. Literature on regional disparities and the concept of convergence is also reviewed. Causes of regional imbalances discussed include natural factors, socio-economic factors, historical legacies, government policies, and geographical conditions.
The objective of this paper has been to experiment diverse economic indicators in order to help equip Ukrainian policymakers with a relatively simple tool, which could deliver warning signals about the possibility of upcoming economic problems and thereby assist the Government in designing policy instruments which would help prevent or soften a slowdown or recession.
Authored by: Vladimir Dubrovskiy, Inna Golodniuk, Janusz Szyrmer
Competitiveness Indicators for Emerging EconomiesConrad Sebego
This document discusses developing competitiveness indicators for emerging economies. It outlines that current indicators do not adequately reflect developing economies and their progress towards goals like the UN's Millennium Development Goals. It proposes developing new indicators that benchmark emerging economies against each other based on their shared challenges and goals. This would allow for more accurate assessment of economic prosperity and potential in developing contexts. The document outlines the objectives, scope, approach and expected outcomes of research to develop these new indicators. It also discusses linking the indicators to government policy and poverty measurement.
Why gender mainstreaming is important?
Formulating public policies with gender budgeting
A case of study: The inclusion of gender budgeting in Bolivia
Best Practices lessons and recommendations
4TH Grade Teacher Participation In Mathematics ContentJoshua Gorinson
This document summarizes key findings from the report "Measuring Innovation in Education 2019: What Has Changed in the Classroom?". It finds that while most countries report educational practices varying greatly, there is likely more innovation occurring than believed but still less than needed to address challenges. A positive trend is the growth of informal teacher professional development and international collaboration, though curriculum reforms have not always translated to classroom changes. Overall, more work is needed to develop systems' capacity to prepare students for the future and better target innovation policies.
This document provides an annual innovation report for New York State. It includes 21 indicators across 6 categories to assess New York's innovation economy. The indicators show that New York ranks in the top third of competitor states for many measures, including venture capital activity, patents awarded, and education levels, though it lags in areas like R&D spending and high-tech employment. The report aims to track New York's progress in building a strong innovation ecosystem through partnerships between industry, universities, and entrepreneurs.
This document discusses statistical analysis and provides definitions and examples. It defines statistical analysis as the process of collecting and analyzing large volumes of data to identify trends and develop insights. It then describes different types of statistical analysis, including descriptive analysis, inferential analysis, prescriptive analysis, predictive analysis, and causal analysis. The document emphasizes the importance of statistical analysis for businesses, researchers, politicians and more. It concludes by explaining some commonly used statistical analysis methods like standard deviation, hypothesis testing, mean, regression, and sample size determination.
This document discusses types of correlation relationships between phenomena. It describes functional relationships where a change in one attribute corresponds to a change in another attribute, and stochastic relationships where this correspondence is probabilistic rather than definitive. Correlation is defined as a stochastic relationship where the average value of one attribute changes with the average value of another. Relationships can be direct or reverse, linear or curvilinear, and involve one or multiple factors. Statistical methods like correlation analysis are used to study and quantify relationships established by theoretical analysis.
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The purpose of the paper is to develop a formal method for monitoring and assessment of the process of sustainable national development. In particular, we apply cluster analysis to develop procedures of formal assessment of development gaps, and show how they can be applied for monitoring processes of sustainable development (the method proposed could also be applied for inter-country comparison and assessment of the impact of technical cooperation projects and different internal and external shocks on national development). A formal method is described and implemented for the analysis of national development processes in the Kyrgyz Republic in the period 1992-2000. The study reveals that although in the second part of the nineties many development indicators in the country improved significantly, the development path observed in this period can hardly be classified as sustainable.
Authored by: Jacek Cukrowski, Julia Mironova
Published in 2002
Building, embedding and reshaping Global Value Chains through investment flow...OECD CFE
Presentation by Oliver Harman, Cities Economist at Cities that Work, Oxford University, UK at the seventh meeting of the Spatial productivity Lab of the OECD Trento Centre held on 20 February 2020.
More info http://oe.cd/SPL
В отчете, который презентовали 29 мая в инновационном парке UNIT.City, были изучены наиболее значимые научные разработки, динамика инновационной активности, интересы международных компаний в Украине, а также украинских изобретателей за рубежом за 2007-2017 года. Проект был реализован совместными усилиями Innolytics Group, SingularityU Kyiv Chapter, UNIT.City, в партнерстве с DLA Piper и Ukrinnovate.
This document provides an overview of the OECD project on Innovation for Inclusive Growth and its 2015 report. The project aims to examine how innovation can promote inclusive development. It involved experts from various countries and organizations. The 2015 report focuses on inclusive innovations that improve welfare for disadvantaged groups. It discusses policy approaches to support such innovations and ensure they reach scale. The document outlines challenges like informality, access to expertise and finance, and regulatory issues. It proposes policy responses like cross-government coordination, public-private partnerships, and financial support to foster cooperation across actors and address challenges.
The problem of managing the R&D sector sustainability in Russia is of particular
relevance in terms of scaling external and internal challenges faced by the country. Such
challenges require an even greater intensification of the efforts to solve the problems
accumulated in Russian economy and innovation system and associated with the state
transition to new technological way. A key criterion of R&D sector competitive
sustainability is the creation of practice-oriented and relevant results of intellectual
activity. Correlation analysis of innovative activity indicators in developed countries over
the 2007-2015 revealed a pattern of two-fold excess of export licenses over the amount of
public investment in science. This dependence is crucial in the study of competitive
sustainability within R&D sector. The method of assessing the efficiency of public
spending on R&D, including an analysis of the dynamics of the growth rate of
performance indicators and their financing in case of R&D completed in the period is
proposed as the main management tool of R&D sector competitive sustainability.
The objective of the PICK-ME (Policy Incentives for Creation of Knowledge – Methods and Evidence) research project is to provide theoretical and empirical perspectives on innovation which give a greater role to the demand-side aspect of innovation. The main question is how can policy make enterprises more willing to innovate? This task is fulfilled by identifying what we consider the central or most salient aspect of a demand-side innovation- driven economy, which is the small and entrepreneurial yet fast growing and innovative firm. We use the term “Gazelle” to signify this type of firm throughout the paper. The main concern of policy-makers should therefore be how to support Gazelle type of firms through various policies. The effectiveness of different policy instruments are considered. For example, venture capitalism is in the paper identified as an important modern institution that renders exactly the type of coordination necessary to bring about an innovation system more orientated towards the demand side. This is because experienced entrepreneurs with superior skills in terms of judging the marketability of new innovations step in as financiers. Other factor market bottlenecks on the skills side must be targeted through education policies that fosters centers of excellence. R&D incentives are also considered as a separate instrument but more a question for future research since there is no evidence available on R&D incentives as a Gazelle type of policy. Spatial policies to foster more innovation have been popular in the past. But we conclude that whereas the literature often finds that new knowledge is developed in communities of physically proximate firms, there is no overshadowing evidence showing that spatial policies in particular had any impact on generating more of the Gazelle type of firms.
Authored by: Itzhak Goldberg, Camilla Jensen
Published in 2014
The Effects of Engineering Education and Government Policy in Driving Innovation among Engineering Graduates in Nigeria by Olawale Oshokoya* in Advancements in Civil Engineering & Technology
The Department of Commerce has determined the economy in the creative industry into 14 (fourteen) sectors: (1) advertising
services, (2) architecture, (3) art and antiques market, (4) craft, (5) design, (6) fashion, (7) video, film and photography, (8)
interactive games (9) music, (10) performing arts, (11) publishing and printing, (12) computer and software services, (13)
television and radio, (14) research and development. Mapping of creative industrial areas is done so that the process of guidance
and distribution of assistance from local and central government and the management of the creative industries from each regi on
focused so that the regions have characteristics in producing the creative industry and can compete in the current era of
globalization. Creative industry in Indonesia began to mushroom in 2007, but many people still do not understand what sectors are
included into the creative industry and its development. With so many people's lack of understanding of the creative industry, it
makes its own homework for the government to socialize the creative industries so that the government seeks to map the creative
industries in Indonesia. Though the creative industry is able to contribute greatly to the Gross Domestic Product (GDP) of an area
and create jobs.
This study aims to further understand the characteristics of creative industries, as well as to identify the constraints and
opportunities faced by creative industry players in Indonesia. To identify and analyze the creative industries used 4 quadrant
statistical methods Location Quadrant (LQ) and Dynamic Location Quotient (DLQ). This study uses secondary data of large and
medium industry statistics as well as small and micro industries in 2010-2013 at Provincial level obtained from the Central Bureau
of Statistics (BPS).
The results of this mapping study will provide a general overview of the creative industries sector that has competitiveness in
every region or province in Indonesia.
Modern Approaches and Strategies for the Implementation of Innovative Process...ijtsrd
The article describes the modern approaches, strategies and the role of innovation in the development of innovation processes in industrial enterprises of the Republic of Uzbekistan. Dexkhanova Nargiza Sharifovna "Modern Approaches and Strategies for the Implementation of Innovative Processes in Industrial Enterprises on the Example of Uzbekistan" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Modern Trends in Scientific Research and Development, Case of Asia , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35882.pdf Paper Url :https://www.ijtsrd.com/economics/other/35882/modern-approaches-and-strategies-for-the-implementation-of-innovative-processes-in-industrial-enterprises-on-the-example-of-uzbekistan/dexkhanova-nargiza-sharifovna
A Study On Determinants Of Private Investment Activity A Case Study Of Debre...Joaquin Hamad
This document summarizes a study on the determinants of private investment activity in Debre Tabor Town, Ethiopia. The study used both quantitative and qualitative methods to identify factors that influence investment from the perspective of 103 private investors. These factors included expected returns, business confidence, interest rates, corruption, savings levels, infrastructure development, and political stability. The study found that these determinants significantly impacted private investment activity in the town. Recommendations were made to policymakers on how to address challenges and promote greater private investment.
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This document provides a proposal for conducting a baseline study in villages in New Tehri district, Uttarakhand, India. The study aims to assess current levels of education, literacy, skills, employment, and use of renewable energy in the region. The proposal outlines a methodology using both quantitative and qualitative research methods, including secondary data analysis, surveys, interviews, and focus groups. It proposes assessing indicators like literacy rates, school enrollment, employment levels, and impact of previous initiatives. The study aims to evaluate current status and measure progress of planned interventions in education, skills training, women's empowerment, and renewable energy development. It provides details on sampling, timelines, deliverables, and a 24-month budget of 17
This document discusses a study that examines the impact of different types of innovation on sales growth in small and medium enterprises (SMEs) in Kosovo. The study collected data from 278 SMEs through surveys. It analyzes the data using logistic regression to test hypotheses about the relationship between innovation types (product/process, marketing, organizational) and firm growth, measured by sales. The findings indicate that marketing innovation is positively associated with sales growth, while new products introduced only within the firm are negatively associated with growth. Other innovation attributes showed no significant relationship. The study aims to provide insights for both theory and policy regarding SME development and innovation in Kosovo.
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This document analyzes socio-economic regional disparities in India. It discusses the need to study disparities as government policies have increased the gap between rich and poor states. Four indicators are selected to identify spatial patterns of regional disparities: population, population density, decadal growth rate, and literacy rate. The methodology involves analyzing secondary data on these and other socio-economic indicators for different districts over time. Literature on regional disparities and the concept of convergence is also reviewed. Causes of regional imbalances discussed include natural factors, socio-economic factors, historical legacies, government policies, and geographical conditions.
The objective of this paper has been to experiment diverse economic indicators in order to help equip Ukrainian policymakers with a relatively simple tool, which could deliver warning signals about the possibility of upcoming economic problems and thereby assist the Government in designing policy instruments which would help prevent or soften a slowdown or recession.
Authored by: Vladimir Dubrovskiy, Inna Golodniuk, Janusz Szyrmer
Competitiveness Indicators for Emerging EconomiesConrad Sebego
This document discusses developing competitiveness indicators for emerging economies. It outlines that current indicators do not adequately reflect developing economies and their progress towards goals like the UN's Millennium Development Goals. It proposes developing new indicators that benchmark emerging economies against each other based on their shared challenges and goals. This would allow for more accurate assessment of economic prosperity and potential in developing contexts. The document outlines the objectives, scope, approach and expected outcomes of research to develop these new indicators. It also discusses linking the indicators to government policy and poverty measurement.
Why gender mainstreaming is important?
Formulating public policies with gender budgeting
A case of study: The inclusion of gender budgeting in Bolivia
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4TH Grade Teacher Participation In Mathematics ContentJoshua Gorinson
This document summarizes key findings from the report "Measuring Innovation in Education 2019: What Has Changed in the Classroom?". It finds that while most countries report educational practices varying greatly, there is likely more innovation occurring than believed but still less than needed to address challenges. A positive trend is the growth of informal teacher professional development and international collaboration, though curriculum reforms have not always translated to classroom changes. Overall, more work is needed to develop systems' capacity to prepare students for the future and better target innovation policies.
This document provides an annual innovation report for New York State. It includes 21 indicators across 6 categories to assess New York's innovation economy. The indicators show that New York ranks in the top third of competitor states for many measures, including venture capital activity, patents awarded, and education levels, though it lags in areas like R&D spending and high-tech employment. The report aims to track New York's progress in building a strong innovation ecosystem through partnerships between industry, universities, and entrepreneurs.
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This document discusses the subject and method of statistics. It defines statistics as the systematic collection, processing, and dissemination of quantitative data on social phenomena. Statistics has three main categories: 1) statistical aggregates, which are large groups of connected social elements or phenomena, 2) units of the aggregate, which are individual elements that make up the aggregate, and 3) attributes, which are measurable properties of the units. Attributes can be qualitative, quantitative, or descriptive. Statistical patterns include dynamics over time, distributions of population characteristics, structural shifts in populations, and interconnections between variables. The law of large numbers states that statistical patterns are more clear and complete when more population units are observed.
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Statistics is the science of collecting, analyzing, and presenting empirical data. It aims to provide reliable information about socioeconomic phenomena and processes to administrative bodies and society through research, identification of relationships, and forecasting of trends. Statistical methods involve mass observation, compilation, calculation of generalizing indicators, and interpretation, and are used across economics, marketing, management, and other social sciences by applying tools from mathematics and probability theory.
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A statistical graph visually represents socio-economic data using geometric shapes and lines. It makes large amounts of data easier to understand by creating images of phenomena and allowing viewers to immediately spot trends and relationships. Graphs serve illustrative, control, and analytical functions, being used to characterize phenomena over time and space, study connections between variables, and compare statistical values. They must accurately reflect source data, be clear and easy to understand, and have an artistic design when possible.
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OJP data from firms like Vicinity Jobs have emerged as a complement to traditional sources of labour demand data, such as the Job Vacancy and Wages Survey (JVWS). Ibrahim Abuallail, PhD Candidate, University of Ottawa, presented research relating to bias in OJPs and a proposed approach to effectively adjust OJP data to complement existing official data (such as from the JVWS) and improve the measurement of labour demand.
2. Elemental Economics - Mineral demand.pdfNeal Brewster
After this second you should be able to: Explain the main determinants of demand for any mineral product, and their relative importance; recognise and explain how demand for any product is likely to change with economic activity; recognise and explain the roles of technology and relative prices in influencing demand; be able to explain the differences between the rates of growth of demand for different products.
5 Tips for Creating Standard Financial ReportsEasyReports
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Solution Manual For Financial Accounting, 8th Canadian Edition 2024, by Libby...Donc Test
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Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
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financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
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• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
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There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
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<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
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Problems and Perspectives in Management, Volume 19, Issue 4, 2021
http://dx.doi.org/10.21511/ppm.19(4).2021.07
The development of innovation at the regional level depends on management decisions. The results of
innovation management are reflected in the innovation competitiveness of the regions. Therefore, to
make successful management decisions, it is necessary to have relevant results of the assessment of the
development of innovation activities. The problem is that it is difficult to conduct assessments at the re-
gional level due to the specifics of individual territories, different scales, and features. Therefore, there is
a certain lack of assessment of the development of innovation at the sub-national level and the innova-
tion competitiveness of the regions. Based on this assessment it is possible to find probable management
decisions for the development of innovation activities at the regional level. It also applies to Ukraine,
where each region has both individual and national characteristics.
1. LITERATURE REVIEW
Innovation activities are organizational, scientif-
ic, technological, financial, and business perfor-
mance, which leads to the introduction of inno-
vations. The development of innovation activities
includes creation, managing, and coordination of
corresponding organizational networks.
In the world, there are various assessments of
the development of innovation activities, which
in most cases assess the structural components
of innovation. There are many kinds of assess-
ments of these components at the macro-level, on
which basis innovative development ratings of
countries and economies are compiled. Existing
assessments mainly include gross product perfor-
mance, investment in innovation, and number of
people engaged in innovations. These assessments
include the Global Innovation Index (Cornell
University et al., 2020), which is a common rank-
ing. In addition to the Global Innovation Index,
there are many other assessments for countries,
among which are the Technology Achievement
Index (United Nations Statistics Division, 2001),
the Innovation Capacity Index (Lopez-Claros
& Mata, 2010), the Bloomberg Innovation Index
(Bloomberg Business, 2015), the Social Innovation
Index (Economist Intelligence Unit, 2016), the
European Innovation Scoreboard (European
Commission, 2020), the European Digital Social
Innovation Index (Nesta, 2021), etc. The advan-
tages of these assessments are that they are mod-
ern and informative for countries. However, as
already noted, there is a certain shortage of such
assessments for the regional level. Significant at-
tempts to take into account the specifics of each
individual territory in assessing the innovation
development of regions were made in particular
for the Innovation Index 2.0, made by the Indiana
Business Research Center (2016). However, this as-
sessment is suited to the territories of the USA for
which it was designed.
The development of innovation activities in the
regions has been studied even before the appear-
ance of mentioned assessments. Nauwelaers and
Reid (1995) analyzed the existing research at the
time to assess the regional technological inno-
vation potential and concluded that regional
innovative databases should be developed. The
continuation of the study in this direction was
in the work of Autio (1998), where it is argued
that regional systems of innovation are signif-
icantly different from national ones, and, thus,
other approaches are called for in the assessment
of regional systems of innovation. Oughton et al.
(2002) investigated the regional innovation par-
adox concerning the contradiction in spending
money on innovation in the regions. Doloreux
and Parto (2005) generalized important ideas
and arguments for theorizing regional innova-
tion systems. Tödtling and Trippl (2005) analyz-
ed different types of regions, considering their
prerequisites for innovation, network and inno-
vation barriers, and developed regional policy
and strategy options.
Arnkil et al. (2010) showed the importance of sup-
porting innovation by regional authorities. Sarkar
(2013) proved that innovation stimulates sustain-
able development of the environmental sector and
promotes green economic growth at all levels.
Sotnyk et al. (2013) investigated the influence of
innovative information and communication tech-
nologies for achieving sustainable development
of the country. Shkarupa (2015) developed an ef-
fective functional system for the phased process
of innovative modernization and management of
socio-economic systems in the region.
4. 79
Problems and Perspectives in Management, Volume 19, Issue 4, 2021
http://dx.doi.org/10.21511/ppm.19(4).2021.07
In subsequent years, significant progress has been
made. Ivanova and Masarova (2016) assessed the
innovation activities of the Slovak regions, using
the variable standardized method. The advantage
of the method is that it considers the relative var-
iability of indicators. The results showed that the
innovation activities of the Slovak regions were
changing continually. Then Ivanova and Kordos
(2017) continued the research and assessed dif-
ferences in innovation activities in individual
regions of Slovakia, using a multi-criteria scor-
ing method. The advantage of the method is its
relative simplicity. The results showed strong dif-
ferences in regions being assessed what is reflect-
ed in their economic performance and competi-
tiveness differences. Stejskal et al. (2018) created
a new method for assessing regional innovation
systems and applied it to individual regions in the
Czech Republic. The advantage of the method is
that it can be easily used practically to map the
development of individual innovative systems in a
region. Horobchenko and Voronenko (2018) ana-
lyzed the impact of innovation factors on sustain-
able development in Ukraine. Kniazevych et al.
(2018) assessed the effectiveness of the innovation
infrastructure of Ukraine using the factor mode-
ling method. The advantage of factor modeling is
its simplicity. The results showed that the degree
of development of innovation infrastructure is
one of the main factors in the development of the
Ukrainian innovation system. Ilyash et al. (2018)
determined the correlation between the volume of
industrial products sold and the main indicators
of innovation in Ukraine using the math mode-
ling method. The advantage of the method is its
accuracy. Wu et al. (2019) assessed the develop-
ment of scientific and technological innovation
at the regional level in China using the method
of principal component analysis. The advantage
of the method is that it reflects most of the infor-
mation of the indicators. The results showed that
the levels and ratios at the regional level had sig-
nificant differences. Boronos et al. (2020) assessed
the level of innovative development of the territo-
ries of Ukraine using methods based on theoreti-
cal and empirical research. The advantage of the
method is that it characterizes the results of the
regional eco-friendly innovation policy. The re-
sults showed that Ukrainian regions are different
from each other both in terms of scientific-tech-
nical and production potentials and in terms of
socio-economic development. Kartanaitė et al.
(2021) showed the necessity of assessment in form-
ing a suggestion for decision-makers in the con-
text of the innovation Industry 4.0.
2. AIMS AND METHODS
The paper aims to assess the development of in-
novation activities in each region of Ukraine and
identify differences in assessment results.
The paper adds some relative indicators for the
assessment of the development of innovation ac-
tivities in the regions of Ukraine that were cre-
ated from selected absolute indicators. Selected
absolute indicators consider the innovation and
research activities of organizations and enterpris-
es, the implementation of innovative products, in-
volvement of employees in innovation research,
innovation costs, and expenses for research and
development (R&D). The development of inno-
vation activities in the regions of Ukraine was as-
sessed using the next four relative indicators:
1. The indicator of involvement of industrial en-
terprises in innovation activities. It is defined
as the ratio of the number of innovation active
industrial enterprises in the region to the total
number of active industrial enterprises in the
region:
= ,
A
I
I
E
I
E
(1)
where I
I ‒ the indicator of involvement of indus-
trial enterprises into innovation activities, A
E –
the number of innovation active industrial enter-
prises in the region, units, I
E – total number of
active industrial enterprises in the region, units.
2. The indicator of innovative productivity of
employees in the implementation of R&D. It
is defined as the ratio of the volume of innova-
tive industrial goods and services sold by the
region to the number of employees involved in
R&D in the region:
,
V
P
E
P
I
N
= (2)
where P
I ‒ the indicator of innovative productiv-
ity of employees in the implementation of R&D,
5. 80
Problems and Perspectives in Management, Volume 19, Issue 4, 2021
http://dx.doi.org/10.21511/ppm.19(4).2021.07
monetary units/person, V
P – the volume of in-
novative industrial goods and services sold by the
region, monetary units, E
N – the number of em-
ployees involved in R&D in the region, person.
3. The indicator of cost-effectiveness for innova-
tion of industrial enterprises. It is defined as
the ratio of the volume of innovative industri-
al goods and services sold by the region to ex-
penses for innovation of industrial enterprises
in the region:
,
V
E
I
P
I
C
= (3)
where E
I ‒ the indicator of cost-effectiveness for
innovation of industrial enterprises, V
P – the vol-
ume of innovative industrial goods and services
sold by the region, monetary units, I
C – expenses
for innovation of industrial enterprises in the re-
gion, monetary units.
4. The indicator of the availability of costs for
performance of R&D. It is defined as the ra-
tio of expenses for performance of R&D in the
region to the number of organizations in the
region that performed R&D:
,
R
S
R
C
I
O
= (4)
where S
I ‒ the indicator of the availability of costs
for performance of R&D, monetary units/units,
R
C – expenses for performance of R&D in the re-
gion, monetary units, R
O – the number of organi-
zations in the region that performed R&D, units.
Increasing of all indicators characterizes the
improvement of the level of the development
of innovation activities. The optimal direction
of changing II
, IP
, IE
, IS
is growth. The first and
fourth indicators characterize the conditions
and factors for innovation activities (input in-
dicators), the second and third indicators char-
acterize the results (output indicators). The rel-
ative character of the indicators makes regions
more comparable, eliminating the impact of the
size of the region.
To smooth out fluctuations of indicators and ana-
lyze them over several years, the average values of
indicators for each region were calculated using
the arithmetic mean method.
Comparing the values of the indicators between
regions, it is possible to assess the competitiveness
of the regions in innovation activities. For this,
the commonly known method of relative assess-
ment of indicators compared to the best of the in-
dicators was used. Since the optimal direction of
changes in all four indicators is an increase, it is
necessary to compare with the maximum values
of the indicators. Relative assessment of compet-
itiveness for each individual indicator II
, IP
, IE
, IS
was performed by the formula:
max
,
I
I
C
I
= (5)
where I
C – the indicator of the relative assess-
ment of the competitiveness of the region in inno-
vation activities for individual indicators II
, IP
, IE
,
IS
, I – the value of indicator II
, IP
, IE
, IS
respectively,
for each region, max
I – the maximum of values of
the indicator II
, IP
, IE
, IS
from all compared regions.
Thus, the normalization of indicators was used.
The indicators of the relative assessment of the
competitiveness of regions in innovation activities
were calculated with average values, average max-
imum values of indicator II
, IP
, IE
, IS
for each region.
To analyze how innovation activities change over
time, the dynamic indices of the development of
innovation activities were designed. They show
whether the regions are moving toward improving
the values of II
, IP
, IE
, IS
. The dynamic indices base
on the geometric mean of the growth rate of indi-
cators II
, IP
, IE
, IS
and are calculated by formulas:
{ }
{ }
1
1
1
1
,
N
I n
N
I
n I n
I
i
I
−
+
−
=
=
∏ (6)
{ }
{ } { }
( )
1
1
1
1
1
,
1
N
P n
N
P
n
P n n
I
i
I r
−
+
−
=
+
=
⋅ +
∏ (7)
{ }
{ }
1
1
1
1
,
N
E n
N
E
n E n
I
i
I
−
+
−
=
=
∏ (8)
{ }
{ } { }
( )
1
1
1
1
1
,
1
N
S n
N
S
n
S n n
I
i
I r
−
+
−
=
+
=
⋅ +
∏ (9)
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where iI
, iP
, iE
, iS
− dynamic indices of the develop-
ment of innovation activities for indicators II
, IP
, IE,
IS
; N – the number of years during which analysis
is conducted; п – the designation number of the
year; r – the inflation rate.
The optimality criterion for iI
, iP
, iE
, iS
is the value
greater than 1.
The study was performed for Ukrainian oblasts,
which are the regions of Ukraine. The data for
Donetsk and Luhansk oblasts are incomplete and
available from enterprises, institutions, and organ-
izations that reported to the state statistics bodies
of Ukraine. The data come from the State Statistics
ServiceofUkraine(2018,2019,2020).TheUkrainian
hryvnia was used as the currency in the calculations.
3. RESULTS
The study was conducted for the period 2017‒2019.
The results for Donetsk and Luhansk oblasts could
be distorted due to incomplete data.
Table 1 presents the results of calculating the indi-
cators of involvement of industrial enterprises in
innovation activities.
As one can see from the results of the calculations
(Table 1), the largest number of innovation active in-
dustrial enterprises in the region in relation to the to-
tal number of active industrial enterprises was in the
Ternopil oblast in 2019, although in the previous two
years the value of this indicator was lower. Except
for Ternopil oblast, also the indicator of Kharkiv
oblast is larger than the indicators of other regions
in 2019. In previous years, the values of the indica-
tor of Kharkiv oblast were even higher. The small-
est indicators have Zakarpattia and Khmelnytskyi
oblasts with values amount to 0.008 in 2019. In pre-
vious years, the values of these regions were also low
and decreased. Chernivtsi and Chernihiv oblasts
have low values of the indicator compared to other
regions. In these regions, over 2017‒2019, the indica-
tors changed towards a decrease. In general, in most
regions, the number of innovation active industrial
enterprises in relation to the total number of active
industrial enterprises is relatively small.
Table 1. Results of calculation of indicators II
by regions of Ukraine, 2017‒2019
Source: State Statistics Service of Ukraine (2018, 2019, 2020).
Region of Ukraine
Indicator II
2017 2018 2019
Vinnytsia oblast 0.019 0.018 0.019
Volyn oblast 0.024 0.018 0.013
Dnipropetrovsk oblast 0.014 0.018 0.015
Donetsk oblast 0.014 0.016 0.018
Zhytomyr oblast 0.016 0.013 0.015
Zakarpattia oblast 0.014 0.012 0.008
Zaporizhzhia oblast 0.021 0.017 0.021
Ivano-Frankivsk oblast 0.021 0.019 0.015
Kyiv oblast 0.012 0.018 0.013
Kirovohrad oblast 0.028 0.028 0.020
Luhansk oblast 0.014 0.010 0.019
Lviv oblast 0.018 0.016 0.015
Mykolaiv oblast 0.023 0.012 0.018
Odesa oblast 0.016 0.011 0.014
Poltava oblast 0.021 0.022 0.022
Rivne oblast 0.009 0.008 0.019
Sumy oblast 0.027 0.027 0.024
Ternopil oblast 0.031 0.024 0.032
Kharkiv oblast 0.031 0.032 0.030
Kherson oblast 0.015 0.014 0.012
Khmelnytskyi oblast 0.008 0.010 0.008
Cherkasy oblast 0.026 0.023 0.022
Chernivtsi oblast 0.014 0.015 0.011
Chernihiv oblast 0.011 0.016 0.011
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Table 2 presents the results of calculating the indi-
cators of innovative productivity of employees in
the implementation of R&D.
Table 2. Results of calculation of indicators IP
by regions of Ukraine, 2017‒2019
Source: State Statistics Service of Ukraine (2018, 2019, 2020).
Region of Ukraine
Indicator IP
, thousand Ukrainian
hryvnias/person
2017 2018 2019
Vinnytsia oblast 725 821 1,374
Volyn oblast 213 1,043 1,086
Dnipropetrovsk oblast 33 132 136
Donetsk oblast 13,874 4,816 36,004
Zhytomyr oblast 380 501 900
Zakarpattia oblast 637 1,159 253
Zaporizhzhia oblast 959 988 720
Ivano-Frankivsk oblast 182 972 294
Kyiv oblast 427 968 575
Kirovohrad oblast 806 2,640 6,071
Luhansk oblast 38 135 2,154
Lviv oblast 163 266 210
Mykolaiv oblast 184 63 645
Odesa oblast 52 309 318
Poltava oblast 206 738 503
Rivne oblast 25 157 24
Sumy oblast 289 458 802
Ternopil oblast 352 1,276 1,136
Kharkiv oblast 169 248 286
Kherson oblast 393 614 814
Khmelnytskyi oblast 73 56 590
Cherkasy oblast 827 1,741 2,095
Chernivtsi oblast 57 67 44
Chernihiv oblast 494 1,334 1,318
According to the calculated indicators of recent
years (Table 2), Donetsk oblast can be considered
the most innovatively productive. Additionally,
Kirovohrad, Luhansk, and Cherkasy oblasts dem-
onstrate relatively large values. Vinnytsia, Volyn,
Ternopil, and Chernihiv oblasts demonstrate
the worst results. Two regions have low values
of indicators in 2019, among them − Rivne and
Chernivtsi oblasts. Generally, it can be stated that
the level of innovative productivity of workers in
the performance of R&D in most regions is rela-
tively low, because one employee engaged in R&D
has a much smaller volume of sold innovative
products than in other leading regions, and this is
a problem for the entire country.
Table 3 presents the results of calculating the in-
dicators of cost-effectiveness for innovation of in-
dustrial enterprises.
Table 3. Results of calculation of indicators IE
by regions of Ukraine, 2017‒2019
Source: State Statistics Service of Ukraine (2018, 2019, 2020).
Region of Ukraine
Indicator IE
2017 2018 2019
Vinnytsia oblast 4.528 1.401 0.794
Volyn oblast 0.412 3.871 2.357
Dnipropetrovsk oblast 0.264 1.662 0.486
Donetsk oblast 4.552 1.501 10.495
Zhytomyr oblast 14.933 1.484 1.433
Zakarpattia oblast 13.641 30.172 3.111
Zaporizhzhia oblast 2.900 0.989 4.110
Ivano-Frankivsk oblast 0.786 3.859 0.615
Kyiv oblast 2.660 2.634 2.794
Kirovohrad oblast 0.804 8.107 7.069
Luhansk oblast 0.649 2.922 17.242
Lviv oblast 2.461 2.982 2.548
Mykolaiv oblast 1.284 0.531 1.028
Odesa oblast 1.049 3.493 3.655
Poltava oblast 3.564 7.106 0.420
Rivne oblast 1.267 9.221 0.255
Sumy oblast 1.006 1.340 0.835
Ternopil oblast 1.157 3.211 0.572
Kharkiv oblast 2.824 2.779 5.331
Kherson oblast 5.122 8.547 5.408
Khmelnytskyi oblast 1.131 1.328 14.065
Cherkasy oblast 4.676 10.207 12.029
Chernivtsi oblast 1.775 0.899 1.746
Chernihiv oblast 4.817 8.204 11.197
From the results of the calculation of indicators for
2017‒2019 (Table 3), the ratio of sold innovative in-
dustrial goods and services to the cost of innova-
tion of industrial enterprises in Donetsk, Luhansk,
Khmelnytskyi, Cherkasy, and Chernihiv oblasts is
relatively large, indicating good cost-effectiveness
of innovation enterprises compared with other
regions. The worst value of the cost-effectiveness
for innovation is in Rivne oblast, for which it is
0.255 in 2019. In addition, relatively low values are
in Poltava and Dnipropetrovsk oblasts, where the
value of the indicator is less than 0.5 in 2019. In
total, there are seventeen regions where the value
of the indicator is greater than 1 in 2019. However,
the cost of innovation has an effect only after a few
years in the form of a significant increase in sales
of innovative products.
Table 4 shows the results of calculating the indica-
tors of the availability of costs for the performance
of R&D.
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Table 4. Results of calculation of indicators IS
by regions of Ukraine, 2017‒2019
Source: State Statistics Service of Ukraine (2018, 2019, 2020).
Region of Ukraine
Indicator IS
, thousand Ukrainian
hryvnias/units
2017 2018 2019
Vinnytsia oblast 2,037 2,336 2,201
Volyn oblast 2,046 2,048 1,658
Dnipropetrovsk oblast 41,888 36,246 40,526
Donetsk oblast 736 955 1,548
Zhytomyr oblast 3,122 3,406 4,395
Zakarpattia oblast 6,905 9,388 7,769
Zaporizhzhia oblast 29,486 53,381 56,980
Ivano-Frankivsk oblast 1,650 3,422 3,008
Kyiv oblast 9,974 13,693 14,919
Kirovohrad oblast 5,041 6,717 2,487
Luhansk oblast 1,955 3,073 3,022
Lviv oblast 4,880 5,928 6,747
Mykolaiv oblast 13,436 13,779 10,756
Odesa oblast 5,630 6,504 6,710
Poltava oblast 2,999 4,036 2,626
Rivne oblast 1,248 1,757 1,468
Sumy oblast 10,044 13,031 7,201
Ternopil oblast 1,477 2,516 3,233
Kharkiv oblast 16,104 22,301 20,605
Kherson oblast 3,505 4,273 3,439
Khmelnytskyi oblast 2,189 2,661 2,137
Cherkasy oblast 4,868 4,929 3,253
Chernivtsi oblast 3,796 4,885 8,133
Chernihiv oblast 3,164 3,517 4,634
The results of the calculation of indicators
(Table 4) show that most regions have relative-
ly low costs for R&D per relevant organization.
However, some regions, such as Dnipropetrovsk,
Zaporizhska, Kyiv, Mykolaiv, Ternopil, and
Kharkiv oblasts, show relatively better results.
Volyn, Donetsk, and Rivne oblasts demonstrate
small values. Note the relatively small devia-
tions in the values of indicators for all regions
over 2017‒2019.
Tables 5 and 6 show the results of calculating the
average values of the indicators II
, IP
, IE,
IS
and val-
ues of the indicator CI
over 2017‒2019 for each re-
gion. Values are ranked in descending order, i.e.
the regions with the best indicators are at the top
of the list.
Table 5. Average values of indicators II
, IP
,
and for them, the values of indicators CI
by regions of Ukraine, 2017–2019
Source: State Statistics Service of Ukraine (2018, 2019, 2020).
Region of
Ukraine
II
CI
for II
Region of
Ukraine
IP
CI
for IP
Kharkiv oblast 0.031 1.000 Donetsk oblast 18,231 1.000
Ternopil oblast 0.029 0.935
Kirovohrad
oblast
3,172 0.174
Sumy oblast 0.026 0.839 Cherkasy oblast 1,554 0.085
Kirovohrad
oblast
0.025 0.806 Chernihiv oblast 1,049 0.058
Cherkasy oblast 0.023 0.742 Vinnytsia oblast 973 0.053
Poltava oblast 0.022 0.710 Ternopil oblast 921 0.051
Zaporizhzhia
oblast
0.02 0.645
Zaporizhzhia
oblast
889 0.049
Vinnytsia oblast 0.019 0.613 Volyn oblast 781 0.043
Ivano-Frankivsk
oblast
0.019 0.613 Luhansk oblast 776 0.043
Volyn oblast 0.018 0.581
Zakarpattia
oblast
683 0.037
Mykolaiv oblast 0.018 0.581 Kyiv oblast 657 0.036
Lviv oblast 0.016 0.516 Kherson oblast 607 0.033
Donetsk oblast 0.016 0.516 Zhytomyr oblast 594 0.033
Dnipropetrovsk
oblast
0.016 0.516 Sumy oblast 516 0.028
Zhytomyr oblast 0.015 0.484
Ivano-Frankivsk
oblast
483 0.026
Luhansk oblast 0.014 0.452 Poltava oblast 482 0.026
Kyiv oblast 0.014 0.452 Mykolaiv oblast 298 0.016
Kherson oblast 0.014 0.452
Khmelnytskyi
oblast
240 0.013
Odesa oblast 0.014 0.452 Kharkiv oblast 234 0.013
Chernivtsi
oblast
0.013 0.419 Odesa oblast 226 0.012
Chernihiv oblast 0.013 0.419 Lviv oblast 213 0.012
Rivne oblast 0.012 0.387
Dnipropetrovsk
oblast
100 0.005
Zakarpattia
oblast
0.011 0.355 Rivne oblast 68 0.004
Khmelnytskyi
oblast
0.009 0.290
Chernivtsi
oblast
56 0.003
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Table 6. Average values of indicators IE
, IS
,
and for them, the values of indicators CI
by regions of Ukraine, 2017–2019
Source: State Statistics Service of Ukraine (2018, 2019, 2020).
Region of
Ukraine
IE
CI
for IE
Region
of Ukraine
IS
CI
for IS
Zakarpattia
oblast
15.641 1.000
Zaporizhzhia
oblast
46,615 1.000
Cherkasy oblast 8.971 0.574
Dnipropetrovsk
oblast
39,553 0.849
Chernihiv oblast 8.073 0.516 Kharkiv oblast 19,670 0.422
Luhansk oblast 6.938 0.444 Kyiv oblast 12,862 0.276
Kherson oblast 6.359 0.407 Mykolaiv oblast 12,657 0.272
Zhytomyr oblast 5.95 0.380 Sumy oblast 10,092 0.216
Donetsk oblast 5.516 0.353
Zakarpattia
oblast
8,021 0.172
Khmelnytskyi
oblast
5.508 0.352 Odesa oblast 6,282 0.135
Kirovohrad
oblast
5.327 0.341 Lviv oblast 5,851 0.126
Poltava oblast 3.697 0.236
Chernivtsi
oblast
5,605 0.120
Kharkiv oblast 3.645 0.233
Kirovohrad
oblast
4,749 0.102
Rivne oblast 3.581 0.229 Cherkasy oblast 4,350 0.093
Odesa oblast 2.732 0.175 Chernihiv oblast 3,772 0.081
Kyiv oblast 2.696 0.172 Kherson oblast 3,739 0.080
Zaporizhzhia
oblast
2.666 0.170
Zhytomyr
oblast
3,641 0.078
Lviv oblast 2.664 0.170 Poltava oblast 3,220 0.069
Vinnytsia oblast 2.241 0.143
Ivano-Frankivsk
oblast
2,693 0.058
Volyn oblast 2.214 0.142 Luhansk oblast 2,684 0.058
Ivano-Frankivsk
oblast
1.753 0.112 Ternopil oblast 2,409 0.052
Ternopil oblast 1.647 0.105
Khmelnytskyi
oblast
2,329 0.050
Chernivtsi oblast 1.473 0.094 Vinnytsia oblast 2,191 0.047
Sumy oblast 1.06 0.068 Volyn oblast 1,917 0.041
Mykolaiv oblast 0.948 0.061 Rivne oblast 1,491 0.032
Dnipropetrovsk
oblast
0.804 0.051 Donetsk oblast 1,080 0.023
Tables 5 and 6 show that Cherkasy and
Zaporizhzhia oblasts are the most competitive
regions in innovation activities over 2017‒2019
in Ukraine: they are at the top of the rankings in
three indicators. Innovation management in these
regions is better. Several other regions − Kharkiv,
Ternopil, Sumy, Kirovohrad, and Vinnytsia oblasts
− are leading in two indicators. Outsiders include
Khmelnytskyi, Chernivtsi, and Rivne oblasts; they
are at the end of the competitiveness ranking in
three indicators. Odesa and Rivne oblasts are at
the bottom of the ranking in two indicators. Most
regions fall simultaneously at the top and bottom
of the list in different indicators.
Since the obtained results do not consider changes
over time, it is necessary to calculate and analyze
the dynamic indices of the development of innova-
tion activities. The results of calculating the dynamic
indices for 2017‒2019 and the ranking of regions by
their values are presented in Tables 7 and 8.
Table 7. Results of calculating dynamic indices iI
and iP
by regions of Ukraine, 2017–2019
Source: State Statistics Service of Ukraine (2018, 2019, 2020).
Region of
Ukraine
iI
Region of
Ukraine
iP
Rivne oblast 1.45 Luhansk oblast 7.04
Luhansk oblast 1.16
Khmelnytskyi
oblast
2.66
Donetsk oblast 1.13 Kirovohrad oblast 2.57
Kyiv oblast 1.04 Odesa oblast 2.31
Dnipropetrovsk
oblast
1.04 Volyn oblast 2.11
Poltava oblast 1.02
Dnipropetrovsk
oblast
1.90
Ternopil oblast 1.02 Mykolaiv oblast 1.75
Vinnytsia oblast 1.00 Ternopil oblast 1.68
Zaporizhzhia
oblast
1.00 Sumy oblast 1.56
Khmelnytskyi
oblast
1.00 Chernihiv oblast 1.53
Chernihiv oblast 1.00 Donetsk oblast 1.51
Kharkiv oblast 0.98 Cherkasy oblast 1.49
Zhytomyr oblast 0.97 Poltava oblast 1.46
Sumy oblast 0.94 Zhytomyr oblast 1.44
Odesa oblast 0.94 Kherson oblast 1.35
Cherkasy oblast 0.92 Vinnytsia oblast 1.29
Lviv oblast 0.91 Kharkiv oblast 1.22
Kherson oblast 0.89
Ivano-Frankivsk
oblast
1.19
Chernivtsi oblast 0.89 Kyiv oblast 1.09
Mykolaiv oblast 0.88 Lviv oblast 1.06
Ivano-Frankivsk
oblast
0.85 Rivne oblast 0.92
Kirovohrad oblast 0.85 Chernivtsi oblast 0.82
Zakarpattia oblast 0.76
Zaporizhzhia
oblast
0.81
Volyn oblast 0.74 Zakarpattia oblast 0.59
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Table 8. Results of calculating dynamic indices iE
and iS
by regions of Ukraine, 2017–2019
Source: State Statistics Service of Ukraine (2018, 2019, 2020).
Region of
Ukraine
iE
Region
of Ukraine
iS
Luhansk oblast 5.15 Ternopil oblast 1.38
Khmelnytskyi
oblast
3.53 Chernivtsi oblast 1.37
Kirovohrad oblast 2.97 Donetsk oblast 1.36
Volyn oblast 2.39
Zaporizhzhia
oblast
1.30
Odesa oblast 1.87
Ivano-Frankivsk
oblast
1.26
Cherkasy oblast 1.60 Luhansk oblast 1.16
Chernihiv oblast 1.52 Kyiv oblast 1.14
Donetsk oblast 1.52 Chernihiv oblast 1.13
Kharkiv oblast 1.37 Zhytomyr oblast 1.11
Dnipropetrovsk
oblast
1.36 Lviv oblast 1.10
Zaporizhzhia
oblast
1.19 Kharkiv oblast 1.06
Kherson oblast 1.03 Odesa oblast 1.02
Kyiv oblast 1.02 Rivne oblast 1.01
Lviv oblast 1.02 Zakarpattia oblast 0.99
Chernivtsi oblast 0.99 Vinnytsia oblast 0.97
Sumy oblast 0.91 Kherson oblast 0.93
Mykolaiv oblast 0.89
Khmelnytskyi
oblast
0.92
Ivano-Frankivsk
oblast
0.88
Dnipropetrovsk
oblast
0.92
Ternopil oblast 0.70 Poltava oblast 0.88
Zakarpattia oblast 0.48 Volyn oblast 0.84
Rivne oblast 0.45 Mykolaiv oblast 0.84
Vinnytsia oblast 0.42 Sumy oblast 0.79
Poltava oblast 0.34 Cherkasy oblast 0.76
Zhytomyr oblast 0.31 Kirovohrad oblast 0.66
From the calculation results of Tables 7 and 8, ac-
cording to the values of the dynamic indices iP
, iE
,
iS
, respectively, 83%, 58%, 54% of the regions of
Ukraine have an increase over time in the values
of three indicators of the development of innova-
tion activities. In such regions the values of dy-
namic indices are higher than 1.
Only 29% of the regions have iI
values greater than
1, which means that the share of innovatively active
industrial enterprises in other regions is decreas-
ing. The iP
index is less than 1 in only four regions,
which indicates a decrease in time over the volume
of innovative products sold by the region per em-
ployee involved in research. Less than half of the
regions have values of the iE
and iS
indices less than
1, which is unsatisfactory for them. Moreover, most
regions show an increase in performance and, ac-
cordingly, they have better competitiveness.
4. DISCUSSION
The obtained values of II
indicators determine the
share of innovatively active enterprises in all consid-
ered regions as small, while the contribution of in-
novatively active industrial enterprises to the overall
development of innovation activities in the region
is the most significant. In many cases, a small val-
ue of the indicator points to a small investment in
innovation.
The results of calculating IP
indicators specify the
productivity of the release of innovative goods and
services per one employee engaged in R&D as rel-
atively low in most regions. This can indicate that
such regions have low integration of education, sci-
ence, industry, and business.
From the results of calculating the IE
indicators, one
can see that the ratio of the volume of sold innova-
tive industrial goods and services to the costs of in-
novation of industrial enterprises in most regions is
relatively small. Although, high costs of innovation
are a positive factor, as they will have a positive effect
in the future.
Based on the obtained data for calculating the IS
in-
dicators, onecan seethatmostregions haverelatively
low costs for performing R&D per organization that
may be due to the underfunding of this area from
both the regional budget and the state. However, this
can indicate that the network of scientific organiza-
tions is not optimal.
The results of calculations of the average values of
indicators II
, IP
, IE
, IS
and the indicators for their
relative assessment of competitiveness in innova-
tion activities demonstrate that no region would
fall to the top of the competitiveness rankings in
all four indicators. Moreover, most regions fall si-
multaneously at the top and bottom of the rank-
ings that indicates the heterogeneity of the devel-
opment of innovation activities and confirms the
results.
The values of iI
, iP
, iE
, iS
indices for most regions are
greater than 1, which indicates that these regions
progress in innovation activities over time and po-
tential for further development. However, regions
with relatively large index values may need large in-
vestments to maintain the pace of innovation. The
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regions with values less than 1 have a declining level
of the development of innovation activities.
The obtained results are generally a continuation
of the research of other scientists. Comparing with
results by Kniazevych et al. (2018), it is found that
the management decisions for the development
of innovation activities in the region are also the
main factors in the development of the Ukrainian
innovation system. Results by Ilyash et al. (2018)
are traced in this study in IP
, IE
indicators for the re-
gions. Boronos et al. (2020) developed normalized
indicators for assessing the level of development of
the territorial innovation system, where the basis of
comparison is the best absolute value of the indica-
tor. It is suitable for assessing the competitiveness in
innovation. Results showed that Ukrainian regions
are different from each other in innovation develop-
ment. The method in this study is quite simple and
at the same time it covers the main areas of innova-
tion activities of the regions with the opportunity to
be improved. Indicators have the relative character
that makes them comparable, eliminating the scope
of absolute values of their components. Some com-
ponents of indicators in any form are used in calcu-
lations of popular global indices, but they are quite
universal, which allows using them at the regional
level. In addition, the method contains dynamic in-
dices that show changes in characteristics over time.
This is especially important in cases where the fac-
tor used in the calculations has a delayed effect. The
results show that in comparison with the existing
results, most regions of Ukraine also have relatively
low values of the indicators of the development of
innovation activities, but over time, most of them
have improved.
CONCLUSION
Assessment of the development of innovation activities in the individual regions of Ukraine using the
relative indicators show that all considered regions of Ukraine have significant differences in results.
For most regions, the values of indicators of the development of innovation activities are relatively low.
The average values of these indicators and the indicators of the competitiveness in innovation activities
demonstrate that most considered regions have heterogeneous development of innovation activities.
However, over time all indicators improved in at least 29% of regions. In addition, on three indicators
out of four, the values improved in at least 54% of regions.
Such differences in results are influenced by the nature of regional management decisions. The goal of
ensuring the high competitiveness of the region through the development of innovative activities is
common at the level of each region since competitiveness and innovation are interdependent. According
to the above, management decisions in all considered regions of Ukraine should be aimed at the im-
plementation of integration processes on an innovative basis in education and science, industry and
business, as well as increasing the level of innovation activity of enterprises, especially in the form of
promoting appropriate investment. It should be increasing in funding for the implementation of R&D,
and the optimization of the activities of organizations engaged in R&D. However, efforts should be
aimed not so much at increasing the costs of innovation, but at increasing the efficiency of these costs.
Management decisions should be comprehensive for all considered regions. For the regions where there
is no improvement over time, the proposed management decisions should be implemented first.
The different competitiveness of the regions reflects the differences in the development of innovation ac-
tivities of the regions. The most notable impact on the development of innovation activities is the fund-
ing of innovation activities. The sufficient funding of innovation activities in each region can be one of
the means of mitigating regional differences. Therefore, it is important to ensure effective funding for
innovation activities at the regional level in Ukraine.
The practical use of the obtained results is the application of the proposed assessment methods, man-
agement decisions regarding the development of innovation activities in the region, and increasing the
competitiveness of the region in innovation activities.
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Opportunities for future research in this direction lie in the plane of identifying specific factors influ-
encing the innovation activities of the most competitive regions. In addition, the study of new sources
of progress in sustainable development based on the development of innovation activities in the regions
is of scientific interest.
AUTHOR CONTRIBUTIONS
Conceptualization: Pavlo Hrytsenko, Viacheslav Voronenko.
Data curation: Pavlo Hrytsenko, Viacheslav Voronenko.
Formal analysis: Pavlo Hrytsenko, Viacheslav Voronenko.
Funding acquisition: Pavlo Hrytsenko, Viacheslav Voronenko, Yevhen Kovalenko, Tetiana Kurman,
Vitalii Omelianenko.
Investigation: Pavlo Hrytsenko, Viacheslav Voronenko, Yevhen Kovalenko, Tetiana Kurman, Vitalii
Omelianenko.
Methodology: Pavlo Hrytsenko, Viacheslav Voronenko.
Project administration: Viacheslav Voronenko.
Resources: Pavlo Hrytsenko, Viacheslav Voronenko, Yevhen Kovalenko, Tetiana Kurman, Vitalii
Omelianenko.
Software: Pavlo Hrytsenko, Viacheslav Voronenko, Yevhen Kovalenko, Tetiana Kurman, Vitalii
Omelianenko.
Supervision: Pavlo Hrytsenko, Viacheslav Voronenko.
Validation: Pavlo Hrytsenko, Viacheslav Voronenko, Yevhen Kovalenko, Tetiana Kurman, Vitalii
Omelianenko.
Visualization: Pavlo Hrytsenko, Viacheslav Voronenko, Yevhen Kovalenko, Tetiana Kurman, Vitalii
Omelianenko.
Writing – original draft: Pavlo Hrytsenko, Viacheslav Voronenko.
Writing – review & editing: Viacheslav Voronenko.
ACKNOWLEDGMENTS
Thepaperispreparedwithinthescientificresearchproject“Sustainabledevelopmentandresourcesecurity:
from disruptive technologies to digital transformation of Ukrainian economy” (No. 0121U100470).
REFERENCES
1. Arnkil, R., Jarvensivu. A., Koski, P.,
& Piirainen, T. (2010). Exploring
Quadruple Helix: Outlining user-
oriented innovation models (Work-
ing Papers 85/2010). University of
Tampere. Retrieved from https://
citeseerx.ist.psu.edu/viewdoc/dow
nload?doi=10.1.1.864.3864&rep=r
ep1&type=pdf
2. Autio, E. (1998). Evaluation
of RTD in regional systems of
innovation. European Planning
Studies, 6(2), 131-140. https://doi.
org/10.1080/09654319808720451
3. Bloomberg Business. (2015). The
Bloomberg Innovation Index.
Retrieved from https://www.
bloomberg.com/graphics/2015-
innovative-countries/
4. Boronos, V. H., Plikus, I. Y., Ku-
bakh, T. H., & Fedchenko, K. A.
(2020). Methodology for assessing
the level of innovative develop-
ment of the territories. Revista
ESPACIOS, 41(07). Retrieved from
http://www.revistaespacios.com/
a20v41n07/20410711.html
5. Cornell University, INSEAD, &
WIPO. (2020). The Global Innova-
tion Index. Retrieved from https://
www.globalinnovationindex.org
6. Doloreux, D., & Parto, S. (2005).
Regional innovation systems:
Current discourse and unresolved
issues. Technology in Society, 27(2),
133-153. https://doi.org/10.1016/j.
techsoc.2005.01.002
7. Economist Intelligence Unit.
(2016). Social Innovation In-
dex. Retrieved from https://
eiuperspectives.economist.com/
technology-innovation/old-prob-
lems-new-solutions-measuring-
capacity-social-innovation-across-
world-0
8. European Commission. (2020).
European Innovation Scoreboard
2020. Retrieved from https://
ec.europa.eu/commission/
presscorner/detail/en/QAN-
DA_20_1150
9. Horobchenko, D., & Voronenko, V.
(2018). Approaches to the forma-
tion of a theoretical model for the
analysis of environmental and
economic development. Journal
of Environmental Management
13. 88
Problems and Perspectives in Management, Volume 19, Issue 4, 2021
http://dx.doi.org/10.21511/ppm.19(4).2021.07
and Tourism, 9(5), 1108-1119. Re-
trieved from https://essuir.sumdu.
edu.ua/handle/123456789/77227
10. Ilyash, O., Dzhadan, I., & Ostasz,
G. (2018). The influence of the
industry’s innovation activities
indices on the industrial products’
revenue of Ukraine. Economics
and Sociology, 11(4), 317-331.
https://doi.org/10.14254/2071-
789X.2018/11-4/21
11. Indiana Business Research Center.
(2016). Driving Regional Innova-
tion. The Innovation Index 2.0.
Retrieved from https://www.stat-
samerica.org/ii2/reports/Driving-
Regional-Innovation.pdf
12. Ivanova, E., & Kordos, M. (2017).
Competitiveness and innova-
tion performance of regions in
Slovak Republic. Marketing and
Management of Innovations, 1,
145-158. https://doi.org/10.21272/
mmi.2017.1-13
13. Ivanova, E., & Masarova, J. (2016).
Assessment of innovation perfor-
mance of Slovak regions. Journal of
International Studies, 9(2), 207-218.
https://doi.org/10.14254/2071-
8330.2016/9-2/16
14. Kartanaitė, I., Kovalov, B., Kubat-
ko, O., & Krušinskas, R. (2021).
Financial modeling trends for pro-
duction companies in the context
of Industry 4.0. Investment Man-
agement and Financial Innova-
tions, 18(1), 270-284. http://dx.doi.
org/10.21511/imfi.18(1).2021.23
15. Kniazevych, A., Kyrylenko, V., &
Golovkova, L. (2018). Innova-
tion infrastructure of Ukraine:
assessment of the effectiveness of
the action and ways of improve-
ment. Baltic Journal of Economic
Studies, 4(1), 208-218. https://doi.
org/10.30525/2256-0742/2018-4-
1-208-218
16. Lopez-Claros, A., & Mata,
Y.N. (2010). The Innovation
Capacity Index: Factors, Poli-
cies, and Institutions Driving
Country Innovation. In The
Innovation for Development
Report 2009–2010. London:
Palgrave Macmillan. https://doi.
org/10.1057/9780230285477_1
17. Nauwelaers, C., & Reid, A.
(1995). Methodologies for the
evaluation of regional innova-
tion potential. Scientometrics, 34,
497-511. https://doi.org/10.1007/
BF02018016
18. Nesta. (2021). The European
Digital Social Innovation Index.
Retrieved from https://www.nesta.
org.uk/feature/european-digital-
social-innovation-index
19. Oughton, C., Landabaso, M., &
Morgan, K. (2002). The Re-
gional Innovation Paradox:
Innovation Policy and Industrial
Policy. The Journal of Technology
Transfer, 27, 97-110. https://doi.
org/10.1023/A:1013104805703
20. Sarkar, A. N. (2013). Promotion
of Eco-Innovation to Leverage
Sustainable Development of
Eco-Industry and Green Growth.
European Journal of Sustain-
able Development, 2(1), 171-224.
https://doi.org/10.14207/ejsd.2013.
v2n1p171
21. Shkarupa, O. V. (2015). Man-
agement of region’s social and
economic development environ-
mental modernization. Economic
Annals-XXI, 7-8(2), 57-60.
22. Sotnyk, I. M., Volk, O. M., &
Chortok, Y. V. (2013). Increasing
ecological & economic efficiency
of ICT introduction as an innova-
tive direction in resource saving.
Actual Problems of Economics,
147(9), 229-235.
23. State statistics service of Ukraine.
(2018). Scientific and innovative
activities in Ukraine. Retrieved
from http://www.ukrstat.gov.ua/
druk/publicat/kat_u/2018/zb/09/
zb_nauka_2017.pdf
24. State statistics service of Ukraine.
(2019). Scientific and innovative
activities in Ukraine. Retrieved
from http://www.ukrstat.gov.ua/
druk/publicat/kat_u/2019/zb/09/
zb_nauka_2018.pdf
25. State statistics service of Ukraine.
(2020). Scientific and innovative
activities in Ukraine. Retrieved
from http://www.ukrstat.gov.ua/
druk/publicat/kat_u/2020/zb/09/
zb_nauka_2019.pdf
26. Stejskal, J., Kuvíková, H.,
Meričková, B. M. (2018). Regional
Innovation Systems Analysis and
Evaluation: The Case of the Czech
Republic. In J. Stejskal, P. Hajek &
O. Hudec (Eds.), Knowledge Spill-
overs in Regional Innovation Sys-
tems (pp. 81-113). Cham: Springer.
https://doi.org/10.1007/978-3-
319-67029-4_3
27. Tödtling, F., & Trippl, M. (2005).
One size fits all?: Towards a dif-
ferentiated regional innovation
policy approach. Research Policy,
34(8), 1203-1219. https://doi.
org/10.1016/j.respol.2005.01.018
28. United Nations Statistics Division.
(2001). Technology Achievement
Index. Retrieved from https://mea-
suring-progress.eu/technology-
achievement-index
29. United Nations. (2015). Trans-
forming our world: the 2030
Agenda for Sustainable Develop-
ment. Retrieved from https://sdgs.
un.org/2030agenda
30. Wu, M., Zhao, M., & Wu, Z.
(2019). Evaluation of development
level and economic contribution
ratio of science and technol-
ogy innovation in eastern China.
Technology in Society, 59, 101194.
https://doi.org/10.1016/j.tech-
soc.2019.101194