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Introduction: Today the ability of nations to develop and implement innovations is core for their international competitiveness. Ukraine is striving for innovation progress; however, its innovation performance is relatively low. The research problem is to find the bottlenecks, affecting Ukraine’s innovation capability.

Purpose: This study aims to research the national innovation capability profiles, based on cluster analysis, to develop an understanding of drivers and threats for the innovation capability of Ukraine.

Need of the study: The knowledge-based economy, which had already turned into one of the most efficient developmental models of the 21st century, became a key driver of international competitiveness for the leading developed countries due to their progressive structural shifts towards the growth of high-technology manufacturing and knowledge-intensive sectors. These trends are significant to capture for the sake of increasing the innovation capability of the economy of Ukraine.

Methodology: The study is based on the K-means clustering method, which is employed for identifying 10 country clusters based on the indicators of their R&D and innovation activities, which allowed us to assess the innovation capability of Ukraine in comparison with 140 countries of the world. Data selection and normalisation were based on the 2019 Global Competitiveness Report indicators.

Findings: The study showed that Ukraine’s innovation capability problems are typical for most developing countries and are prevalently connected to low R&D expenditures, patent applications, and international co-invention activities. Most countries, except for the technologically developed ones, follow the so-called ‘passive technological learning’ strategies, which usually result in low economic productivity.

Practical implications: Several innovation policy implications have been developed for the government of Ukraine based on the cluster analysis results and accounting for the problems of the national innovation system (NIS).

One of the most profitable industries worldwide is automotive manufacturing, with an estimated economic impact of 2.9 trillion dollars, which employs 8% of the economically active world population, both directly and indirectly. The main 12 world powers in the automotive sector account for 80% of the total manufactured vehicles, and Mexico ranks seventh in the world and is part of the free trade agreement in the Pacific Alliance (PA). This fact may have a direct impact on boosting the domestic automotive industry in Mexico by leveraging the manufacturing supply chain in primary and secondary industries in the three commercial partners: Chile, Colombia, and Peru. This chapter found that there is a coincidence in the competitiveness pillars of the Mexican automotive industry and its business partners in the PA. This presents a strategic panorama of competition and economic development for the region.

This chapter discusses the positioning of Belarus in the international context of socioeconomic development based on an assessment of the country's dynamics in world rankings. The country's presence in the recognized world rankings and its holding high positions in them is an obvious advantage for achieving a favorable investment image. Ratings characterize the country's comparative position at the international level in a number of areas: from credit capacity to human capital development.

There has been analyzed the position of the Republic of Belarus in several recognized international comparisons, such as Human Development Index, Doing Business, ICT Development Index, Global Innovation Index, Sustainable Development Goals Index, Corruption Perceptions Index, Rule of Law Index, Worldwide Governance Indicators, and others.

However, Belarus is not yet participating in the international competitiveness assessment through such popular international ratings as Global Competitiveness Index and Global Entrepreneurship Monitor. The research findings show that the strongest aspects of the socioeconomic development of Belarus are in place due to the high educational level of the human capital development, gender equality, and the implementation of the UN sustainable development goals. The analysis also shows that the weaknesses of institutional environment and public administration do not enable the full implementation of the planned goals of socioeconomic development.

Purpose: The paper aims to examine the link between the indicators of competitiveness of World Economic Forums (WEFs) in the area of higher education, on the one hand, and the Global Competitiveness Index 4.0 and ‘Future Orientation of Government’ sub-pillar, on the other.

Method: Consideration of the link between the mentioned link will be conducted on a sample that includes European Union member countries and potential members. The key information basis for conducting this research makes data from the Global Competitiveness Report 2019 of the WEF. The research was conducted by applying multiple regression analysis.

Findings: The results of the research show that there is a positive correlation between higher education, on the one hand, and the level of national competitiveness and sustainable development, on the other.

Practical Implications: The obtained results of empirical research can serve as a ‘global benchmark’ of future public policy in the field of higher education in the analyzed countries.

Originality/Value: The key contribution of this paper is reflected in providing a clearer insight into the competitiveness factors of the analyzed countries that are expressed in the field of sustainable development and higher education and pointing out the priority activities of competent state authorities to their improving and raising the level of national competitiveness.

The unequal distribution of economic activities, transposed in economic, social and territorial disparities is the general characteristic of the European economy. Gaps increased in the context of European Union (EU) enlargement towards Eastern and Central Europe and of the economic crisis, thus bringing new differentiations among member states’ economies. The main aim of the chapter is to emphasise the centre-periphery differentiations in the European economy, by using a composite index of peripherality, in order to better understand the determinants of growth and convergence in Central and Eastern European countries and to reach normative conclusions for increasing Cohesion Policy (CP) effectiveness. The first part of the chapter provides a short overview of the main theories and models of the peripherality analysis and the relationships between the centre and the periphery, in order to find out how this analysis relates to the research in the field. The second part provides a comparative analysis of the evolution of European economies during 2003–2014, in order to find out whether the EU enlargement process stabilised the EU core-periphery pattern or, on the contrary, the process of core-periphery structural convergence occurred. The third part includes the suggested model of analysis (methodology, data, and main results) from a multidisciplinary perspective, underlining the centre-periphery differentiations on the two axes, North–South and West–East. The results have been interpreted in conclusions, with a focus on their relevance for the European CP challenges.

Globalization speeds up competition among nations in various sectors. In terms of multinational and transnational phenomena, countries are seen as inescapable from competition, thus the linking of the term global with “competitiveness.” The research described here explores the relationship between the competitiveness of a country and its implications for human development. For this purpose, using data envelopment analysis (DEA) and cluster analysis, 44 selected countries were evaluated. An output-oriented super-efficiency model where global competitiveness indicators are taken as input variables with human development indicators as output variables is utilized. Then cluster analysis depending on the competitiveness and human development indicators is conducted by using self-organizing maps to specify the development levels of the countries. Both analyses are repeated for years between 2005 and 2007. Finally, the relationship between the super efficiency scores and the development levels is analyzed.

This chapter is devoted to sustainable human resource management that leads to sustainable competitiveness. It features the ways human resources can be managed to carry out sustainable goals and the impact of sustainability on employees' attitudes and behaviours. The aim of this study is to explore the complex objectives of sustainability and human resource management and empirically investigate the dynamic relationship between human resources in science and technology and sustainable competitiveness in the case of 35 European countries. Our contribution emphasizes this interrelationship and its causality. For this research, we applied a vector auto-regression (VAR) model, and the Granger causality method to examine the relationship between human resources in science and technology and sustainable competitiveness. A panel data included 314 observations between 2012 and 2021. The panel VAR for analysing the impulse response function was enriched with the 5% and 95%, using Monte Carlo simulations. The research results revealed bidirectional causality in the European countries between human resources in science and technology and sustainable competitiveness. Human resources in science and technology trigger sustainable competitiveness and vice versa. As an element of originality, our study demonstrates that human resources in science and technology contribute to sustainable performance, and, on the other hand, a more competitive and sustainable environment contributes to the development of human resources in science and technology. Thus, the chapter outlines the role of human resources in science and technology with regard to sustainable human resource management (HRM), and how to navigate these objectives so that they can positively influence sustainable competitiveness.

This article discusses the work of the Global Peace Index and how peace itself can be characterized in order to analyze its relationship with society. This article explores the various notions and definitions of peace which exist, such as the differences between “Positive Peace” and “Negative Peace.” Peace cannot simply be thought of as “the absence of violence,” there are many complex aspects to take into consideration and which influence the creation of peaceful societies, including political stability, economics, types of government, and business environments, to name but a few.