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This study aims to establish the position of the European Union (EU-28) countries in the dynamics of international trade openness linkages and the Global Competitiveness Index (GCI) in correlation with the gross domestic product (GDP) per capita, research and development (R&D) expenditures, innovation capability and information and communication technology (ICT) adoption.

In the panel data set, comparative analyses were applied to scatter diagrams, correlation and regression analyses and structural equation models using Eurostat and World Economic Forum (WEF) data for the EU-28 countries in the period 2008–2019.

The empirical results did not confirm the hypotheses that a positive correlation exists between GCI and trade openness indicators and between GDP per capita and GCI. The ICT adoption and innovation capability increase GCI, which affects GDP per capita.

The empirical results provide a better understanding of the importance of trade policies, particularly in terms of trade openness and trade shares of the EU-28 countries, as it could contribute to increasing the GCI of the EU-28 countries. Furthermore, the results of this study underline the importance of ICT adoption and innovation capability and the need for appropriate government policies that improve global competitiveness.

This study, through empirical analysis, demonstrates the existence of correlations between trade openness (exports as % of GDP, imports as % of GDP and export market shares as % of world trade), R&D expenditures, innovation capability, ICT adoption, GDP per capita and the GCI in the EU-28 countries. In addition, this study contributes managerial and policy-based implications on driving forces of global competitiveness.

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).

This paper aims to explore the construction of a valid and reliable measure for the competitiveness of cities that excludes the drivers of competitiveness from the index construction. Not incorporating these drivers in the index avoids the problem of assuming relative contributions (i.e. weights) of these drivers on competitiveness as a maintained hypothesis.

From the definition that competitiveness is the ability of a city to sustain prosperity, this study derives a model called the hedonic well-being index (HWI) in which prosperity is measured by using the consumption of goods and service including leisure. This study then uses secondary data sources to construct an exploratory HWI (assuming a Cobb Douglas functional form) and compare this index to three benchmarks, namely, income, gross domestic product (GDP) per capita and the World Happiness Report (WHR) index. This study also review the component expenditure of the index across geographical locations.

The HWI is better predicted by the WHR index (a subjective well-being index) than by the GDP per capita (a measure of output), owing to the inclusion of leisure and household production absent in per capita GDP. This study explored and found regional variations in the distribution of the expenditure components in the HWI.

This paper demonstrates the feasibility of constructing an exploratory HWI to measure the competitiveness of cities using secondary data. The reliability of the index can be improved using primary data in future research. Separating the drivers from the definition of competitiveness allows testing of the contribution and interaction of these drivers on competitiveness.

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 research paper aim at providing a new approach of calculating the destinations competitiveness index. How can these variables been aggregated in other to reflect the realities of very distinct productive environments? We assume that: The weighting of variables provides a better measure of destinations competitiveness. Base on the Neo-Technological theory, after a life cycle differentiation, we used a panel data approach to calculate the weight of each variable as the spearman correlation coefficient of its contribution to tourism inflows growth. After integrating these weights, we came to the point that by applying an appropriate weight to its components, we end up having a competitiveness index that significantly improve the correlation between competitiveness and tourism inflows growth.

The paper aims to relook at the notion of competitiveness in the challenging times of pandemic. The failure of global value chains caused many nations despite their state of economic development to struggle for necessary items. The goal of achieving profitability abated by having self-sufficiency, specifically in the manufacturing sector where unlike services, the production processes jolted. Under this backdrop, the paper attempts to project local industry clusters as a panacea for achieving resilient competitiveness.

To explore the credibility of clusters as an important tool to prepare nations towards a resilient future, a correlation between the state of cluster development in a country and its national competitiveness has been tested. Besides, any difference in the notion of state of cluster development between least developing countries, developing countries and developed countries has also been tested. The paper uses the data from World Economic Forum’s Global Competitiveness Report.

Strong support for the research hypothesis has been found. High positive correlation exists between the state of cluster development and national competitiveness. The state of cluster development is found to be significantly different among three categories of countries.

Clusters have been promoted in academic literature, policy circles and the business community for many advantages. This paper is a novice attempt to showcase these can serve as an instrument to prepare economies for self-sufficiency and a resilient future by performing an objective evaluation.

The objective of this study is threefold–first, to develop a Regional Competitiveness Index (RCI) for measuring competitiveness of sub-national regions for India; second, to test this index for its ability to explain regional growth, which validates usage and applicability of this index; and third, to further investigate if the competitiveness of states is in turn caused by economic growth, i.e. it is tested if there is a bidirectional causality between competitiveness and regional growth.

The data of indicators used in the index are from sources available freely in public domain. The competitiveness index is constructed using equal weightage supported by principal component analysis (PCA) technique. The causal relationship analysis is done using panel data of 10 years from 2008 to 2017 for 32 Indian states/union territories. The generalized method of moments (GMMs) is used for this dynamic regression estimation.

Based on RCI score, states have been ranked and through rank analysis, the authors observe the performance status of these sub-national regions and are able to categorize them as improving, no change or deteriorating in regional competitiveness. Using the GMM estimation, the association between RCI and economic growth is found to be significant at 10% level. This shows that regional competitiveness as captured through the RCI score is able to explain regional economic growth and economic disparity among the sub-national units. Further, that RCI score is found to Granger-cause growth, while growth does not lead to better RCI scores. This establishes the usefulness of RCI as an important policy variable to compare states and provide direction for sectoral reforms.

The limitations of the study include (1) broad assumption that these sub-national regions belong to a uniform macro-economic and technology environment, and (2) data constraints as it is a longitudinal study. The study implies that the composite index could capture differences in regional competitiveness explaining regional economic disparity and that competitiveness causes higher economic growth and not vice versa.

The RCI score can prove to be a useful indicator of economic performance of different states and can be used by national and state policymakers to compare and assess regional disparity among different states. The pillar-wise scores will be useful for in-depth study of weakness and strength of the sub-national territories.

Construction of an RCI for sub-national territories and analysis of panel data for longitudinal study of ten years is unique in the regional competitiveness literature.

Based on the rankings of the global competitiveness index and the fragile states index, this paper aims to suggest alternative approaches to shed some light on the effectiveness of rankings in helping emerging economies improve their competitiveness from an institutional standpoint.

The statistical analysis consisted of a two-stage analysis; the first stage consisted of constructing an updated Alternative Institutional Quality Index (AIQI), intending to design a comparative measure between dimensions over time. The second stage consisted of evidencing the structure of each of the observed dimensions' variance to evidence the existing changes or gaps of the AIQI and its components. The authors incorporated the Kruskas–Wallis (KW) model to test the results.

This paper demonstrates that the analyzed countries generally maintain their competitive position, even though changes in their scores are reflected. This makes invisible the development and progress factors generated by the countries that are mainly found with low scores and only reflect stable structures that allow them to maintain their position.

The current study has a limitation because it concentrated on a few selected indicators based on the literature review. The limitations of this research may be overlooked in the future by adding additional variables and observations. The paper could be improved by including intra- and inter-regional approaches to control based on the occurrence of specific circumstances (i.e. informal institutions, economic development or factor endowments).

The paper contributes to the applicable measurement of competitiveness and its structural change over time.

This paper proposed an alternative and simple methodology to assess the evolution of the competitiveness indicators; this methodology could be used to measure structural changes at different levels, which may be an input for the design and implementation of policies to foster competitiveness.

National intellectual capital is generally considered a strategic advantage for national competitiveness. However, the measurement of intellectual capital across countries for comparison purposes appears to receive little attention. This study aims to use a new index of national intellectual capital (INIC) to examine the relationship between national intellectual capital and national competitiveness.

This paper uses the INIC, developed by Vo and Tran (2021), to measure, compare and contrast differences in the level of national intellectual capital across 104 countries. INIC comprises the most crucial intellectual capital components: human capital, structural capital and relational capital. Various economic and social indicators are used as the proxies for these components of intellectual capital. Principal component analysis is used to derive INIC.

The results indicate that during the study period the level of national intellectual capital gradually increased. Europe has attained the highest level of national intellectual capital, whereas Africa has achieved the lowest level. This study’s findings confirm a close relationship between the national intellectual capital level and the national income level. Among the ten biggest countries, the USA achieved the highest national intellectual capital level, and China has significantly improved its cumulative level. Finland achieved the highest level of national intellectual capital in the world. National intellectual capital enhances a country’s competitiveness.

Findings in this study shed light on an international comparison of intellectual capital across countries and understanding how national intellectual capital contributes to and improves national competitiveness. Policymakers can consider and use these findings to support the accumulation of national intellectual capital and boost national competitive advantage, especially low-income countries and emerging markets.

To the best of the authors’ knowledge, this is the first study to estimate a degree of national intellectual capital around the world and examine its impact on national competitiveness based on publicly available data.