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.
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