Search results

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.

The purpose of this paper is to examine how the ranking of countries based on the World Economic Forum's (WEF') competitiveness index is changed when the underlying primitive data dimensions of this composite index are aggregated using weights that are endogenously determined for each country, instead of aggregated using the WEF's fixed set of weights applied to all countries.

The paper presents a method based on data envelopment analysis to determine weights for aggregating the underlying primitive data dimensions of any composite indicator. The approach determines endogenously the “best” weights a given observational unit (e.g. country) on the basis of its revealed performance on each primitive sub‐dimension underlying a composite index. The ranking of countries based on the values of a composite competitiveness index that uses the proposed endogenous weight method is then compared to the ranking based on the WEF's competitiveness index for the year 2006. The rankings are then compared and assessed to determine if the observed difference in the rankings are statistically significant.

A comparison of the ranking of countries on the basis of the value of each index reveals that countries do undergo a change in their competitiveness rank when endogenous weights are used. The results suggest the WEF's competitiveness index, which uses the same fixed weights applied to every country (or group of countries), creates a bias that favors countries that score high on the “technology” sub‐dimension of the index.

The study presents an alternative to the current practice of using a fixed set of weights applied uniformly to the basic unit of analysis. The method serves as a starting‐point for further research on the biases created by different weighting schemes to construct a composite indicator that aggregates primitive data, with the resulting composite index values then used to rank entities.

The method to determine endogenously the weights to be applied to each unit of analysis when constructing a composite indicator is novel and has wide applicability to the general issue of comparing performance across different units of analysis based on a composite index of performance (i.e. benchmarking).

This paper was aimed to develop better knowledge to show how obstacles impact Sustainable Development Goals (SDGs) in investment business on the global competitiveness index (GCI). This study was applied to six Gulf Cooperation Council (GCC) economies to analyse and classify investment obstacles in order to improve GCI and mitigate the obstacles to doing business.

This study used the 12 pillars of the GCI to classify six GCC countries and 15 factors of SDGs using data from 2008 to 2017. The data were collected from the International Monetary Fund and GCI reports from 2008 to 2018 on all six GCC countries: the UAE, Kuwait, Oman, Saudi Arabia, Bahrain and Qatar. The paper adopted equations to analyse the GCI, along with 15 obstacles to doing investment business. The paper used regression and correlation tests by two proxies: obstacles to SDGs as an independent variable and the GCI as a dependent variable.

The findings of this study focussed on the best classification of the GCI, which went to Qatar, whereas the lowest rank went to Oman. The major components of obstacles to doing investment business are restrictive labour regulations, access to financing and inefficient government bureaucracy factors. These obstacles stand in the way of achieving SDGs and delay the improvement of the competitive field. Hence, the results of the regression test show that there is a negative and statistically significant impact in Oman, Kuwait and the UAE between obstacles to doing business on the GCI at the significance levels of 1% and 5%. The Pearson correlation matrix is strong between obstacles to SDGs, as the same elements of the GCI also exist in these countries, at 55.2%, 75% and 55.5%, respectively.

There are some limitations related to the study period being from 2008 to 2017. Before 2008, the GCI consisted of nine pillars rather than 12, and there were 14 problems rather than 15 related to doing investment business. Hence, this does not match with the period of this study. Furthermore, the reports after 2017 did not mention the problems of doing business, only analysing the GCI.

The results of the study highlight the strategic and practical aspects of GCC countries diagnosing the SDGs to know how to reduce obstacles to sustainable development, which can enhance investments by improving the GCI.

The current study measured and evaluated how to mitigate the obstacles to SDGs in the GCC countries. It is the first study to explain these obstacles in the GCC countries, which are characterised by their huge wealth that contributes significantly to global economic development.

The purpose of this paper is to emphasize that the growing of competitiveness at any level may be possible through more responsibility (business ethics) on the one hand and less corruption (as lack of business ethics) on the other.

The objective of the paper is to identify the double‐way relationships between competitiveness and the responsible (beyond ethics) behaviour. In order to do this, the authors used correlation indexes CORREL and R² and the graphic representation able to illustrate the above‐mentioned interrelations.

The authors observed that there is a strong and direct correlation between GCI, RCI and CPI – at national level, and six possible situations which reflect the interrelations between NP and FGP.

The paper may be continued with specific behavioural models of MNEs in different host countries – integrating different approaches of business ethics.

The practical implications of the paper consist in offering some guidelines/starting points for firms in the search of global competitiveness through responsible/ethical conduct.

The paper develops a new conceptual framework, which integrates two “obsessions” of nowadays (competitiveness and responsibility) into the concept of global performance – national and firm related.

The purpose of this study is to assess how the recent financial and economic crisis has affected European Union (EU) member states’ ability to attract intellectual capital. The issue was found to be relevant, as one of the key elements of competitiveness today is the ability to attract intellectual capital and the question how the recent financial and economic crisis has changed this ability of EU member states can be asked. The question is relevant in relation to the diversity of effects that the crisis had on EU member states, including, the different levels of real economy adjustment constraints.

The concept of competitiveness applied by the World Economic Forum (WEF) in constructing the Global Competitiveness Index (GCI) was used. Based on selected WEF GCI sub-indicators and the WEF’s methodology, we a new index named “Ability to attract intellectual capital” was generated. EU member states’ performance was compared along this indicator for the 2007-2008 (pre-crisis) and the 2013-2014 (post-crisis) periods. In this way, EU member states can be ranked before and after the crisis; their performance can be compared in the two periods, relatively to each other, and in relation to their performance along other relevant indices.

The findings show interesting results. First, many peripheral EU member states, deeply affected by the crisis, could considerably improve their relative positions between 2007 and 2013. Second, the Central and Eastern Europe (CEE) countries show a rather mixed picture, drawing up rather different individual development paths. Third, the advancements in some countries do not imply that overall convergence is proceeding in the EU. Nevertheless, some countries have not wasted the “good” crisis to take those steps of structural reform.

Because we only look at two time periods (pre-and post-crisis), the authors are not able to describe the processes that were going on in the EU member states during the years of the crisis; the results can only show the difference between the two periods. Furthermore, there may be other methodological approaches to countries’ abilities to attract intellectual capital that may bring results different from this study’s results. For the countries who, according to our investigations, could improve these abilities, enhanced competitiveness is likely to occur in a few years’ time.

For those countries aiming at improving their abilities to attract intellectual capital, or for EU policy design, this research may provide useful results. Moreover, not only this study’s results but also the methodology can be used by others, for other purposes: to compare different years, different sets of countries included in the WEF GCI or even along different dimensions.

This study’s research findings, the authors believe, will help EU member states and the EU as a whole in getting to know their abilities to attract intellectual capital better. In the introductory part of this paper, the aim was also to collect arguments from the economic theory to explain why such abilities are crucial for future competitiveness of countries.

The methodology that was used is the adoption of WEF methodology, and the data are from the WEF GCI dataset. However, to the authors’s knowledge, no other research work has applied this methodology on this set of WEF GCI sub-indicators, with such purposes as to compare EU member states’ abilities to attract intellectual capital before and after the crisis.

This paper aims to investigate the smart economic development (SED) patterns in Europe in relation to competitiveness. Motivational focus corresponds to global events: the fourth industrial revolution, transition to a low-carbon economy, economic shocks (such as the 2008 financial crisis, Brexit or the coronavirus pandemic), which requires rethinking development policies, targeting competitiveness increase and reducing imbalances in economic development.

The analysis includes self-organising neural networks cluster analysis and correlations, comparative analysis of SED indicators structure and cumulative index estimation with World Economic Forum (WEF) global competitiveness index. The panel data set of 19 years from 2000 to 2018 for 30 European countries.

Overall, cross-country examination suggests that European countries of higher competitiveness illustrate higher estimates in SED. The key determinants are juridical fairness, social responsibility, competence building, intelligence and welfare employment to develop smart patterns for reaching higher competitiveness.

The limitations relate to the particular sample of European countries and gathering statistical data and a methodology of the SED index calculation. In addition, the paper contains a macroeconomic environment focus on competitiveness estimation. Further research may be improved with micro and mezzo environment incorporation at a cross-country analysis level.

By linking well-known terms of competitiveness and economic development with a concept of smartness, new approaches to policymaking emerged. The methodology presented in this paper has implications for territorial cohesion policies, competitiveness and branching strategies. The combination of SED sub-indexes and WEF GCI might aid a more accurate ex ante measurement.

The findings are essential for fostering a smart approach in economic development for long-term competitiveness.

This paper provides original empirical evidence about the relationship between SED and competitiveness and adds new knowledge that smartness becomes a way for building countries’ competitiveness by identified two profiles of SED patterns by development stages, namely, integrated to economic development and institutional-based which is divided to focus and balanced.

This paper aims to explain the empirical relationship between competitiveness and economic growth in a globalizing world. In recent times, the advanced economies have experienced a slowdown of growth, whereas the BRICS countries continue to experience high growth. The authors explore the following question: Does competitiveness of nations’ degree of competition explains this differential in growth? The authors explore competiveness and growth in a macroeconomic perspective for the large economies in the OECD and BRICS countries.

The authors use dynamic panel data modelling technique to find the relationship between competitiveness and economic growth. This technique enables to control heterogeneity problem of this group to some extent. The focus variable of this study is annual GDP growth rate for the period 2007-2017. The proxies for measuring competitiveness in this paper are trade as percentage of GDP, product market regulation, unit labour cost and global competitive index. Innovation prevalence of foreign ownership, efficiency, competition, state of cluster development, venture capital availability, extent of market, research and development expenditure as percentage of the GDP mergers and acquisitions and multifactor productivity are the control variables.

The authors find that the degree of competitiveness competition is less likely to impact economic growth in the OECD countries because they have more or less similar competitive environment. Innovation, extent of market and state of cluster development and venture capital availability explaining growth differential. Increased competition is likely to affect growth negatively. This explains the oligopolistic structures of the world economy. However, the BRICS countries vary significantly in competitive environment. This is the reason of volatility in their growth. The conclusion is that competitiveness is important for sustained growth. Competitiveness is, however, an outcome of a set of policies, not a policy itself.

Productivity data for OECD and BRICS countries are not available. Various series are not comparable. OECD countries have discontinued yearly unit labour cost series, and high frequency series are available but no such series for BRICS exists.

First, this paper proposes that wage growth, measured by the unit labour cost growth rate, is an important determinant of competitiveness amongst the nations. Wage growth is falling short of productivity growth in the OECD countries. This has implications for the long run sustainability of growth, skill development and inequalities in the region. Since 2011, world economic recovery is slow. Wage growth is imperative for generating sufficient private demand in the OECD countries. Second, this paper provides evidence that competitiveness is important for explaining growth in the OECD and the BRICS countries. However, it also highlights that competitiveness can be measured effectively by the trade differential or with the help of unit labour cost. Unaligned real effective exchange rate in terms of unit labour costs is the real cause of the problem.

Research in this area is still in infancy. This research finds that how competitiveness affects growth. A more competitive nation can sell more, but not necessarily grow rapidly. In development process, growth comes first, and at the latter stages, countries have to introduce effective reforms for competitiveness. This is the effect of competitiveness on growth by comparing various indexes.

This study aims to evaluate the competitiveness of nations and seeks to answer the following research question: how does the competitiveness of nations include improvements in the quality of life, thus influencing and contributing to social progress in both social and economic indices?

This paper collected secondary data from the World Economic Forum and the socioeconomic dimensions of the Social Progress Imperative Index and considered the dimensions of these indices, which were demonstrated using Pearson’s correlation coefficient. The main focus was on the documentary analysis that was carried out to explain the realities of 121 countries from 2014 to 2017 as taken from these indices, considering the 10 countries at the top and bottom.

This study showed the use of new measures for the performance of nations that are less dependent on economics and focus more on social development, which may be a trend for the future of nations, and produce a more holistic view for the study. “Innovation” is the factor with the weakest relationship with social progress, which is justified by a weaker relationship with one of the subcategories, “basic human needs”, when analyzed in isolation. However, when the authors analyze the best and worst nations, the authors observe that economic factors are still prevalent, with the “institutions” and “infrastructure” factors being effective for improving competitiveness and the quality of life.

The findings represent a new, emerging configuration in country performance, but the study has its limitations, such as the use of only two pooled variables and the fact that it does not correlate their dimensions or variables.

This study can represents an expansion logic for measuring the performance of countries considering social factors. The main contributions of this study are its statistical evidence and documentary analysis of the relationship between economic and social variables. The main contribution of this paper is to show that over time (2014–2017) economic factors, as measured by the competitiveness index of nations, relate to aspects of social welfare, as measured by the social progress index.

The purpose of this paper is to study the linkage of innovation and entrepreneurship to economic growth in countries with different levels of development.

Following quantitative analysis, the authors carry out three empirical approaches to examine the effects of innovation and entrepreneurship on competitiveness. In accordance with their initial study framework, they test the conceptual model of competitiveness through applying descriptive statistics, structural equation modelling (SEM) and hierarchical cluster analysis. Descriptive statistics and SEM data sources from the Global Competitiveness Report of the World Economic Forum were analysed for 148 countries. The hierarchical cluster analysis furthermore analysed Global Entrepreneurship Monitor data on 67 different countries.

The study confirmed that innovation and sophistication factors are crucial to the competitiveness of economies. The study also revealed the definition of five clusters relative to the competitive performance of advanced economies following the introduction of new entrepreneurship variables.

This research aims to open up avenues for the development of regional competitiveness studies.

This paper represents a comparative study of five Asian countries, namely, Singapore, Taiwan, South Korea, China and India, based on the Global Competitiveness Index (GCI) 2015-2016 published by the World Economic Forum. The purpose of this study is to assess India’s position vis-a-vis the various comparator Asian economies and to identify areas for improvement so as to enhance India’s competitiveness.

The study is based on the comparisons and analysis of the ranks of each country. These ranks are based on the indicators related to three categories, i.e. “Basic Requirements”, “Efficiency Enhancers” and “Innovation and Sophistication” Factors. The GCI included data from internationally recognised agencies such as the IMF, the WHO and the United Nations Educational, Scientific and Cultural Organization.

On the basis of the aforementioned comparisons among these five Asian economies, it was found that Singapore (Rank-2) has made stupendous economic progress and is amongst the top five successful economies of the world. Taiwan, South Korea and China also have taken significant economic strides and are ranked globally at 15, 26 and 28, respectively. India, on the other hand, is ranked 55 out of 140 nations.

In this paper, the countries were compared on the basis of their rank in the GCI Report 2015-2016. For an in-depth and more holistic study, comparison can be done by taking into consideration other important reports and analysis in this regard.

This is an original study where the developments that have taken place in the five Asian economies have been analysed based on the GCI. Most importantly, this study identifies the area/indicator in which India needs to improve to be placed among the developed nations.