Mapping Key Competencies for the Knowledge Society Era

The objective of this paper is to evaluate the innovation and digital competitiveness of the EU-27 countries and to reveal the effect of innovation performance on the changes in digital competitiveness. We obtained secondary data from the annual reports of the Global Innovation Index (GII), Summary Innovation Index (SII), and Digital Economy and Society Index (DESI) for the years 2017 to 2021. Using structural equation modeling, the main concept was to develop a new variable, Innovation Performance (IP), which was created by combining the indices of the GII and SII dimensions within a new three-factor model. The modelling findings revealed that both indexes had a statistically significant impact on the variations in the IP value, with the SII exerting a greater influence compared to the GII. The IP variable in the modified model has a significant positive impact on the change in the score of the DESI. We found a positive impact for 10 dimensions (from 19) and a negative impact for firm investments. The highest positive impact was confirmed for research systems, information technologies, and human resources. Our findings also highlight real economic contexts and drivers for EU countries' strategies and policies in the progress of digital competitiveness.

Digitalization involves an increase in the use of information and communication technologies (ICT) in all areas of the economy and all domains of the functioning of a society. Technologies of this kind affect the level of competitiveness of economies. The aim of the article is to compare the levels of competitiveness of European Union countries in the field of information and communication technologies, on the basis of indices developed by international institutions.The European Commission, World Economic Forum and Eurostat databases were used for comparative analysis of economies. Synthetic indices, such as the 9th pillar of the Global Competitiveness Index (GCI Pillar 9), the European Digital Economy and Society Index (DESI) and the Networked Readiness Index (NRI) were used to compare the levels of digitalization of the economies. The actual individual consumption (AIC) value was adopted as an indicator of the wealth of EU economies. Changes in single indices were analysed as follows: in the NRI in 2014–2016, in the GCI Pillar 9 in 2015–2017 and in the DESI in 2016–2018, while the multi-character classification of countries according to the three variables (the NRI, DESI and GCI Pillar 9) was performed for the year 2016. Ward's hierarchical method and non-hierarchical analysis of k-means clusters were used to this effect. The multiple regression model revealed relationships between the welfare level measured by the AIC and the level of digitalization. The NRI turned out to be the best predictor. The results of the analysis indicate that there are still differences between the ‘old’ and the ‘new’ EU countries in terms of the development of the ICT sector.

The aim of the article is to present measuring methods of the knowledge -based economy (KBE) by two ways of measuring, i.e. on the basis of the KBE synthetic indicator and the KBE advancement evaluation in Poland. The methods are as follows: a) Knowledge Assessment Methodology with use of the Knowledge Economy Index (KEI) and the Knowledge Index (KI); b) European Innovation Scoreboard which allows to assess the KBE development level using the Summary Innovation Index (SSI). The analysis of the indices proves that Poland occupies distant places in rankings. In Poland should be prepared a long-term KBE development strategy which should contribute to setting up an effective system of creation and diffusion of innovations.

The European Commission (EC) has monitored Member States' digital progress through the Digital Economy and Society Index (DESI) since 2014. The DESI index currently ranks the EU Member States and monitors their progress based on four core and 33 individual indicators. We sought to determine whether convergence between the Member States could be detected using the DESI’s annual databases. By examining the variation in the indices, we propose the existence of a so-called “Matthew effect”, i.e., the “rich get richer” syndrome among the 27 EU Member States. We also hypothesised that the COVID-19 pandemic would influence the change in the DESI. Issues investigated were those using bibliometric, statistical-mathematical methods. The σ-convergence analysis was used to estimate the reduction over time of the differences between the Member States, while the β-convergence analysis was used to estimate the rate of catching up with the initial level of development. A PCA analysis was performed to verify the Mathew effect with additional λ-variances considering real GDP per capita change. The σ-convergence was confirmed over the period 2016–2021. The β-convergence was significantly confirmed, and the research also revealed that the half-life of catching up is approximately 20 years. The suggestion of a Matthew effect in the 2016–2021 period, although not significantly confirmed, tends to suggest its existence. The COVID-19 pandemic’s impact on the value of the DESI index is likely to be affected, but future studies are needed to find support for this hypothesis. The study concludes that convergence between the EU-27 Member States can be detected based on the DESI, but this does not imply convergence for all four core DESI indicators.

The paper’s primary goal is to analyse the development of Knowledge Economy (KE) measurement methods ranging from those based on national income to indices identifying and combining the relevant indicators. The paper focuses on four current global and European KE level indices: Global Innovation Index (GII), Global Knowledge Index (GKI), European Innovation Scoreboard (EIS), and Digital Economy and Society Index (DESI), highlighting persistent significant differences in the perception of the very essence of KE, as there is no clear interdisciplinary definition of the initial concept of knowledge, leading to further problems with ambiguous and insufficiently specific definitions and measurement of KE. Tacit aspects of knowledge are rarely part of KE definitions or measurements, excluding a large part of the knowledge system from KE measurements. The results of the analysis show that the set of KE indicators used by the individual KE indices is heterogeneous, with the set of intersecting indicators having different weights in terms of importance. Frequent interventions in the indices by their authors were observed, such as changes in index methodology, the indicators used, main pillars (subindices), etc. Despite the high heterogeneity in the approach to measuring KE, we identified the pillars, which can be viewed as the core pillars of KE. These include, for example the level of ICT, R&D, human resources, innovation, patents, and education.

Visegrad Group is a group of four countries in Central Europe, namely the Czech Republic, Slovakia, Poland, and Hungary. These countries share not only a similar history, but also similar economic development (measured for example by Gross Domestic Product (GDP)) and geo-political ideas. Nowadays, the economic development of every country and its competitiveness on the world market is supported by the creation of innovation (knowledge-based economy), especially from an Industry 4.0 point of view. The aim of this article is to compare the Visegrad Four (V4) from different perspectives. Firstly, the comparison of GPD development is done, next the analysis of foreign trade. The article presents the results of a comparative analysis of changes in innovativeness and competitiveness of the V4 economies over a period of 5 years. The Global Innovation Index (GII) shows the level of innovation of most countries in the world. Reports publishing GII were established thanks to the cooperation of Cornwall University with INSEAD (fr. Institut européen d'administration des affaires) Business School and World Intellectual Property Organization. The Summary Innovation Index (SII) was used in the European Innovation Scoreboard, as well as the Global Competitiveness Report and Global Competitiveness Index (GCI). The analysis shows that all members of V4 are so called moderate innovators. The Czech Republic begins to diverge from other member states in terms of SII, GII and it has been increasing its GCI as well. Poland occupies one of the last positions in the V4 innovation ranking, where Hungary was the weakest in terms of competitiveness in 2016. However, the mutual connection between GDP and above mentioned indexes shows relatively surprising results.

The paper examines the innovative development of Belarus in the context of international indicators and ratings of innovation. International indicators of innovation are becoming an important tool for evaluating the effectiveness of innovation policy. Innovation policy often suffers, especially in developing countries, from an insufficient understanding of the complex phenomenon of innovation. Lack of a systemic approach to innovation leads to a lack of the emphasis on innovation based on knowledge from any source and not just on the knowledge formally created through R&D. Identified are the strengths and weaknesses of innovation policy of Belarus, as well as the problems of innovative development given the Global Innovation Index, the Innovation Union Scoreboard and Knowledge Economy Index. Developed are the new directions of innovation policy for Belarus.

This chapter uses relevant secondary data and the findings in chapters 4 and 5 to discuss the implications of poor systems of innovation in the Arab region. In addition to examining the characteristics and causes of poor national and regional systems of innovation in the Arab region from national and regional perspectives in chapters 4 and 5 , and before we provide recommendations for enhancing systems of innovation in the Arab region in chapter 7 , this chapter discusses the implications of poor systems of innovation in this region. This complements our analysis in chapters 4 and 5 on the causes of poor systems of innovation and provides in-depth analysis to examine the serious consequences and implications of poor systems of innovation in the Arab region according to certain criteria, mainly the classification of Arab countries according to economic structure. This chapter addresses the third question mentioned previously: what are the major implications of poor regional systems of innovation in the Arab region? This chapter also examines the fifth hypothesis, which argues that poor systems of innovation in the Arab region have serious implications in terms of poor S&T output indicators (patents, publications, share of high-technology exports), competitiveness, GCI, FDI, knowledge economy index (KEI), technology infrastructure, technology achievement index, global innovation index, innovation quality indicators, innovation efficiency ratio, ability to do business, and capacity of creation and absorption of knowledge in the Arab region. It also investigates the sixth hypothesis, which is that natural resources (the oil, mixed oil, and primary export economies)-based economies have experienced more serious implications as compared to the diversified economies in the Arab region.

The European Union (EU) annually publishes an Innovation Union Scoreboard (IUS) as a tool to measure the innovation performance of EU Member States by means of a composite index, called the Summary Innovation Index (SII). The SII is constituted by an average of 25 indicators. The SII is claimed to rank Member States according to their innovation performance. This means that the higher the average value of the 25 indicators, the better the innovation performance is said to be. The first purpose of this article is to assess whether the SII constitutes a meaningful measure of innovation performance. Our conclusion is that it does not. Our second purpose is to develop alternative, productivity or efficiency-based, measures of innovation system performance based on a simple index number, and complement it with advanced and robust nonparametric Data Envelopment Analysis techniques. By doing so, the article offers a critical review of the SII, and proposes to put more emphasis on the identification of and relation between input and output innovation indicators. The data provided by the 2014 and 2015 editions of the IUS are here used to analyze the innovation performance of all 28 EU national innovation systems. A theoretical background and reasons for selecting the indicators used are presented, and our new ranking of the innovation performance using bias-corrected efficiency scores of all EU countries is calculated. We find that the results differ substantially between the SII and the ranking based on our method, with significant consequences for the design of innovation policies. (Less)

The Western Balkan countries have set themselves the goal to join the European Union as soon as possible. Accordingly, they must adjust the key components of their development policies to the Europe 2020 strategy, focusing on key priorities such as smart, sustainable and inclusive growth. This paper explores the relationship among knowledge economy readiness, innovativeness, and competitiveness of six Western Balkan countries (Albania, Bosnia and Herzegovina, Macedonia, Serbia, Croatia, and Montenegro) and the group of six selected neighboring EU countries (Austria, Bulgaria, Greece, Hungary, Romania, and Slovenia). The paper relies on the data obtained from the Knowledge Economy Index of the World Bank Institute, INSEAD's Global Innovation Index and the Global Competitiveness Index of the World Economic Forum for 2013. Obtained data from all three sources indicated significantly lower readiness for the development of economy based on knowledge, innovation and competitiveness in the Western Balkans countries in comparison to the selected EU countries. The analysis of the interdependence of the aforementioned variables points to: a) statistically significant correlation between the indicator knowledge economy index and the global innovation index for both groups of countries; b) statistically significant linear correlation between innovativeness and innovation efficiency ratio for the Western Balkan countries. Conversely, no respective correlation has been registered for the group of selected EU countries; c) no statistically significant correlation between the global innovation index and the global competitiveness index in the Western Balkan countries, while in respect of the group of selected EU countries, the existence of significant linear correlation between these variables has been revealed.

The world experience proves that the development of the innovational society is directly linked to the concept of human capital through the development of the sectors where the accumulation of human capital takes place and primarily in the sphere of education and science. This paper is an exploratory research based on the analysis of macroeconomic indicators on digital performance, highlighting the progress made in terms of digital competitiveness, as well as on the digital framework and systemic conditions for education. The educational system is in a continuous dynamic; to cope with changes, this system is forced to make intensive use of creative techniques at all levels of it. However, in this context, many teachers are concerned about standardised processes and tests, compared to creative ones. Creativity has been and is combined with science: art and technology turn people’s ideas into facts. At the beginning of the twenty-first century, the importance of innovation for growth (especially economic growth) was recognized, but the question of non-technological innovation is also raised. To this end, consideration was given to the processing and systematisation of secondary data taken from official reports prepared and published by the European Commission. Thus, we refer to the reports of the Digital Economy and Society Index (DESI) and the Human Capital Index (HCI) with reference to Romania, in the time horizon 2018-2020. The DESI 2020 reports are based on 2019 data. The UK is still included in the DESI 2020 and the EU averages are calculated for 28 member states. The DESI has been recalculated for previous years to reflect changes in the choice of indicators and corrections to the data. Human capital is a key trigger for sustainable, inclusive economic growth, but investment in health and education has not received the well-deserved attention. The index establishes a direct link between improved outcomes in health and education, productivity and economic growth.

The article examines the phenomenon of digitalization as a key area of digital development. It is established that digitalization is a digital transformation of life, society and business. This is a very important process in terms of active technology development. It is noted that the essence of digitalization is the digitization of services, trade, documents and all spheres of life. Prerequisites for the formation and effective functioning of a competitive environment should be the absence of discrimination and equality of all agricultural businesses and certain segments of the agricultural market. In this way, people will be able to carry out all the necessary processes in electronic format: buy goods, take out insurance, receive documents, etc. It is emphasized that since 2014 the European Commission has been monitoring the digital progress of member states by calculating the Digital Economy and Society Index (DESI). DESI 2021 indicators are analyzed and it is established that the progress achieved in the EU member states in digital development in such areas as human capital, broadband, integration of digital technologies by enterprises and digital technologies, public services is monitored. All Member States have made progress in digitalization, but the overall picture for Member States is ambiguous, and despite some convergence, the gap between EU leaders and countries with the lowest DESI remains significant. The most significant progress compared to last year can be seen in Ireland and Denmark, followed by the Netherlands, Spain, Sweden and Finland. These countries also perform well above the EU DESI average, based on their DESI 2021 scores. In general, Denmark, Finland, Sweden and the Netherlands have the most developed digital economies in the EU, followed by Ireland, Malta and Estonia. Romania, Bulgaria and Greece have the lowest DESI rates. Despite these improvements, it is clear that all Member States will need to make a concerted effort to achieve the 2030 targets set by the Digital Decade for Europe. It is established that Ukraine is only taking the first steps in this new reality. The positive thing is that we are moving. On the negative side, these steps are often carried out according to standards that are incomprehensible to EU countries, and integration with which is another strategic goal of Ukraine.

Motivation: The rapid digital transformation in the European Union has highlighted the increasing importance of the digital economy in shaping national competitiveness. Recent studies underline the relevance of innovation, human capital, and digital infrastructure as critical factors driving digitalisation. Despite extensive literature on digital economy development, there is a gap in understanding the specific determinants that influence the digitalisation levels across EU member states. This study addresses this gap by examining the relationship between digital infrastructure, human capital, and innovation in fostering digital growth, using the Digital Economy and Society Index (DESI) and European Innovation Scoreboard (EIS) as key indicators. Aim: The aim of this research is to identify and quantify the key determinants influencing the development of the digital economy in the EU member states between 2017 and 2022. Specifically, the study investigates the impact of technological infrastructure, human capital, and intellectual assets on the digital economy. By employing multiple linear regression models, the research aims to clarify how these factors contribute to digital economy growth. Results: The analysis revealed that technological infrastructure and use of IT are consistently the most significant determinants of digital economy development. Human capital, particularly in terms of education and digital skills, gained importance in the later years of the study. Intellectual assets, such as patents and research outputs, also played a critical role, particularly from 2021 onwards. The findings suggest that countries with robust digital infrastructure, well-educated workforces, and strong innovation ecosystems tend to perform better in terms of digitalisation as measured by DESI. These insights provide valuable guidance for policymakers aiming to enhance digital competitiveness through targeted investments in technology, education and innovation.

The use of artificial intelligence is becoming the most important factor in the development of any state. Legal regulation of the order of its application has become the most important task of modern foreign and Ukrainian law. Analysis of the international practice of implementing various methodological approaches and tools for measuring the digital economy, statistical data, in particular the Digital Economy and Society Index (DESI), is important from the point of view of introducing such approaches in Ukraine as well. 21 March 2024 — The United Nations General Assembly has unanimously adopted the first global resolution on artificial intelligence to encourage the protection of personal data, the monitoring of AI for risks, and the safeguarding of human rights. As part of its digital strategy, the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits, such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy. In April 2021, the European Commission proposed the first EU regulatory framework for AI. It says that AI systems that can be used in different applications are analysed and classified according to the risk they pose to users. The different risk levels will mean more or less regulation. On March 13, 2024, the European Parliament formally adopted the EU Artificial Intelligence Act (“AI Act”). The AI Act is the world’s first horizontal and standalone law governing AI, and a landmark piece of legislation for the EU. The EU considers the AI Act as one of its key pieces of legislation and fundamental to the EU. More specifically, the EU is aiming for the AI Act to have the same ‘Brussels effect’ as the GDPR - in other words, to have a significant impact on global markets and practices and to serve as a potential blueprint for other jurisdictions looking to implement AI legislation. However, the story does not end here. Over the next few months and years, the AI Act will be specified and supplemented further by secondary EU legislation — implementing and delegated acts to be adopted by the EU Commission. The article is devoted to the study and research of individual legislative initiatives of some foreign countries (such as the Netherlands, Singapore, as well as the European Union) and Ukraine in the field of digitization and artificial intelligence. The main problems and challenges of introducing artificial intelligence in the context of human rights protection are highlighted. Keywords: digitization, artificial intelligence, artificial intelligence strategies, digital economy and society indices, risks of using digital technologies.

The use of augmented reality games (ARGs) in education has gained increased attention from curriculum developers, teachers, and researchers in the past decade. Research findings show that ARGs can promote meaningful learning environments that foster key competences for the 21st century. This paper presents the design process of “EcoAegean”, an ARG for mobile devices, which was implemented in primary classroom environments to support the development of students’ key competences in the context of sustainability. The game was created using an open augmented reality software platform and its design was based on contemporary theoretical underpinnings regarding the use of such games in educational contexts. In the first section of the paper, we describe the design procedures of the learning scenario as well as the game itself. In the last section of the paper, we offer a set of critical insights on the design and implementation of mobile augmented reality games for the purpose of supporting students’ development of key 21st century competences.