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The issue of a location's attractiveness for business development in literature lacks approach, when attractiveness is assessed not as a set of factors which determine individual attractiveness, but as a locality's ability to attract, maintain, and create business and investments. The contribution of the research to the discipline is a multi-criterion model of factors determining the location's attractiveness for business development in the context of smart growth, as a methodological tool to evaluate and analyse the scientific problem in a question which is proposed by us. The attractiveness of a location for business development in the model is combined with the concept of smart development. A new and reliable instrument for decision-makers and managers is presented. An example of panel data analysis of 36 indicators and 3600 observations from 10 cross-sections of annual data for determining the role of quantitative indicators in attractiveness index is provided and timing lags influence is assessed. The method proposed is suitable for the attractiveness analysis of any location if the necessary data is available.

Academic scholars agree that increasing urbanization and intensive technological progress raises new issues in urban development trends. This paper defines the characteristics of future city and analyses the specifics and role of infrastructure in it. Future city development is based no more on infrastructure growth but on its effectiveness and quality which may be achieved only by installing newest technologies and implementing strategic management.

. Income inequality and population’s migration are economic processes ongoing in every country, but their scales are different. Although both phenomena – income inequality and population’s migration – earn sufficient scientific attention, scientific literature is still lacking comprehensive studies on interdependence between them. This research is aimed at the assessment of the impact of income inequality on population’s migration. This article highlights how significant it is to assess the impact of income inequality on population’s migration, and reviews the issues of income inequality and population’s migration previously analysed in scientific studies. The research is based on the methodology developed for the EU Member States. The paper provides original perspective as the EU Member States are divided into six groups by their income inequality and net migration rates and the impact of income inequality on population’s migration is researched in particular groups of the current EU Member States by applying the methods of correlation and regression analysis. The results of the research indicate that the impact of income inequality on population’s migration differs within and between the EU Member State groups. Research results revealed that, income inequality has a more significant impact on population’s immigration than on emigration in all EU Member State groups. Income inequality causes population’s emigration only in the states with medium income inequality rates. The paper contributes to the scientific literature of regional development as the quantitative analysis of the interconnection between income inequality and population’s migration is scarce.

A rich volume of literature has analysed country investment attractiveness in a wide range of contexts. The research has mostly focused on traditional economic concepts—economic, social, managerial, governmental, and geopolitical determinants—with a lack of focus on the smartness approach. Smartness is a social construct, which means that it has no objective presence but is “defined into existence”. It cannot be touched or measured based on uniform criteria but, rather, on the ones that are collectively agreed upon and stem from the nature of definition. Key determinants of smartness learning—intelligence, agility, networking, digital, sustainability, innovativeness and knowledgeability—serve as a platform for the deeper analysis of the research problem. In this article, we assessed country investment attractiveness through the economic subjects’ competences and environment empowering them to attract and maintain investments in the country. The country investment attractiveness was assessed by artificial intelligence (in particular, neural networks), which has found widespread application in the sciences and engineering but has remained rather limited in economics and confined to specific areas like counties’ investment attractiveness. The empirical research relies on the case of assessing investment attractiveness of 29 European countries by the use of 58 indicators and 31,958 observations of annual data of the 2000–2018 time period. The advantages and limitations of the use of artificial intelligence in assessing countries’ investment attractiveness proved the need for soft competences for work with artificial intelligence and decision-making based on the information gathered by such research. The creativity, intelligence, agility, networking, sustainability, social responsibility, innovativeness, digitality, learning, curiosity and being knowledge-driven are the competences that, together, are needed in all stages of economic analysis.

A system of 36 regional-level indicators is selected to create a new index measuring a location's attractiveness for business development (the LA-index), on the criteria of intelligence, networking and infrastructure, sustainability, digitalisation, learning, agility, innovativeness and knowledge. Business establishment is defined by nine indicators. Overall, the research data include 5,170 observations. The methodology presented is suitable for assessing the attractiveness of any region if the necessary data are available. We use correlation analysis and the Granger causality test to analyse the impact of business attractiveness on the establishment of new businesses. The main findings reveal that attractiveness for business development has a positive impact on the establishment of new businesses, but the determinants and time lags of this impact vary depending on the level of economic development of the region. The paper contributes to the regional economic development literature by exploring the concept of a location's attractiveness using the smartness approach, and by discovering the time lags of the impact of this on the establishment of new businesses. The paper provides original empirical evidence, which helps policy makers to develop more accurate strategies and decision-making process based on smartness determinants and delayed effect (impact over time).

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Social implications The findings are essential for fostering a smart approach in economic development for long-term competitiveness. Originality/value 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.

The chapter is designed to stimulate a discussion on a new approach that combines quantum theory with artificial intelligence in the analysis of the economic development of socio-economic systems. The chapter introduces the specifics of the modern socio-economic system and the challenges to economic development. After that, the chapter discusses the possibility and compatibility of approaches (quantum theory) and tools (artificial intelligence) for analysing economic development. The chapter contributes to a new approach in economic development theory by integrating quantum theory and artificial intelligence possibilities. Additionally, the competences needed to use artificial intelligence in the analysis of economic development are presented. The value of the chapter is in its contribution to the original methodological justification of the use of quantum theory and artificial intelligence in the analysis of economic development.