Investment Attractiveness. The Case Of The Visegrad Group Countries

Dorożyński, Tomasz; Kuna‐Marszałek, Anetta

doi:10.1515/cer-2016-0007

Cited by 18 publications

(16 citation statements)

References 32 publications

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“…Table 1 presents the concept of factors determining a country's investment attractiveness in regard to foreign direct investment, being a universal methodology, which may be used to analyse territories at different hierarchical levels. In the empirical analysis, 29 European countries from inside and outside the European Union were selected, which is why such an indicator as the availability of EU resources under the budget is not included in Table 1, unlike in Dorozynski and Kuna-Marszalek [56].…”

Section: Methodsmentioning

confidence: 99%

“…The assumption was made that the impact of the European Union and other countries' funding are incorporated in the whole result and process of economic development and separate determinants, in acceptance that the availability of subsidies and incentives from European Union resources significantly impacts the investment attractiveness of the countries [56].…”

Section: Methodsmentioning

confidence: 99%

“…Countries try to create such investment attraction mechanisms [53,54] or such characteristics [55] that make them more attractive compared with other countries. Countries compete for investments by offering investors favourable local conditions or even adapting them to the needs of investors [56]. In the definition of foreign direct investment, the International Monetary Fund emphasised the aim of establishing a lasting interest by the investor in the economy of another country (investment recipient).…”

Section: Investment Attractiveness: a Smartness Approachmentioning

confidence: 99%

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Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?

et al. 2019

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Investment Attractiveness: a Smartness Approachmentioning

confidence: 99%

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Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?

et al. 2019

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“…The catching up processes of the V4 countries in the period between 2000 and 2015 initially have similar characteristics, which can be attributed primarily to the pre-accession processes, the inflow of foreign capital and the accession in 2004 (Dorożyński, Kuna-Marszałek 2016;Farkas 2016). After the crisis of 2008, the catching up process of the Slovakian and Polish economies stayed continuous, while the Hungarian and Czech economies basically stagnated until 2012 and became more dynamic only from 2013, from which point all four countries converged with the EU15 average at a similar rate ( Fig.…”

Section: The Catching Up Processes Of the Visegrad Countries And Theimentioning

confidence: 98%

The Catching up Processes of the Regions of the Visegrad Group Countries

Lengyel

Kotosz

2018

CER

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The majority of Central and Eastern European post‑socialist countries acceded to the European Union in 2004. The integration of these economies to the Union had begun earlier, which was strengthened by grants from the Structural Funds after the accession. One of their aims is to facilitate the catching up processes of less developed regions and their convergence to the average of older member states. In our study1, we examine the success of the catching up processes of the NUTS3 regions in the four Visegrad Group countries (V4), i.e., the Czech Republic, Hungary, Poland and Slovakia, between 2000 and 2014 to the average of the 15 initial member states of the European Union. Is there a process of catching up in each region, and if so, is it at a similar or a highly different rate? We analyze the development of GDP per capita at Purchasing Power Parity, and we examine disparities in the level of catching up using entropy‑based Theil indexes. We provide a detailed analysis of two of the influencing factors of the catching up process of regions. Firstly, we look at whether the catching up process of the regions took place in a similar or very different way compared to the national average. Secondly, we examine how the size of the biggest city of the regions affected catching up, and whether the role of the biggest city of region can be shown.

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“…Foreign scientists also have different views on investment attractiveness (Dorożyński, Kuna-Marszałek, 2016;Michalet,1999;Jáč, Vondráčková, 2017;Stecenko, Buka, 2014;Kharlamova, 2014;Tancosova, 2014;Kersan-Škabić, Tijanić,2014;Popescu, 2013;Slavik, 2013). Thus, Dorożyński and Kuna-Marszałek consider the investment attractiveness of a country or a region to be a combination of the advantages associated with its location and some specific characteristics of a specific area.…”

Section: Literature Reviewmentioning

confidence: 99%

Factors improving investment attractiveness Of Russia’s depressed regions

Palkina¹,

Kislitsina²,

Kirov³

et al. 2018

AMP

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Recent development of the Russian economy is largely determined by strengthening of interregional socio-economic differentiation. The uneven development of spatial socio-economic systems, in turn, makes it possible to talk about the so-called "prosperous" and "problem" regions. The problem or depressed regions are, as a rule, characterized by relatively poor adaptation to existing business conditions, lower competitiveness of industries operating in these regions, an increase in unemployment rates, negative social phenomena, and others. It should be noted that the Russian scientists studying the regional economy have already developed a list of indicators that allow one to identify the depressed regions quite accurately. These indicators include the industrial production index, unemployment rate, per capita GRP, and per capita industrial output. In accordance with the indicators and on the basis of the statistical data of recent years, ten constituent territories of the Russian Federation are classified as depressed regions: Ivanovo Region, Oryol Region, Smolensk Region, Pskov Region, Volgograd Region, Chuvash Republic, Kirov Region, Ulyanovsk Region, Kurgan Region, Altai Krai. To maintain the upward trend in the Russian economy, it is necessary to ensure an increase in investment, including investment in the economy of the depressed regions, which requires enhancing their investment attractiveness. The traditional measures of a general spectrum, which should contribute to enhancing the investment attractiveness of any territory, do not usually produce the desired result in these regions. In this respect, there is an urgent need to study the factors of investment attractiveness of the regions in order to identify the most significant ones. This will make it possible to develop and justify the proposals regarding the government investment policy, which can ensure a considerable investment in flow into the economy of the problem territories.

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Investment Attractiveness. The Case Of The Visegrad Group Countries

Cited by 18 publications

References 32 publications

Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?

Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?

The Catching up Processes of the Regions of the Visegrad Group Countries

Factors improving investment attractiveness Of Russia’s depressed regions

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