A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

Plasma Lipid Levels In Turkish Adult Haemophilia Patients

8th Annual Congress of the European-Association-for-Haemophilia-and-Allied-Disorders -- FEB 11-13, 2015 -- Helsinki, FINLANDWOS: 000348680100141European Assoc Haemophilia & Allied Disorder

A decision support system to evaluate the competitiveness of nations

The aim of this chapter is to explore methodological transparency as a viable solution to problems created by existing aggregated indices as well as to conduct a detailed analysis on the ongoing performance of nations’ competitiveness. For this purpose, a methodology composed of three steps is used. To start with, a combined clustering analysis methodology is used to assign countries to appropriate clusters. Unlike the current methods that use a single criterion, the proposed methodology uses 135 criteria for a proper classification of the countries. Relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, the countries are ranked based on weights generated in the previous step. As a final analysis, the dynamic change of the rank of the countries over years has also been investigated

Preclinical Marker of Atherosclerosis in Patients with Haemophilia: Carotid Intima Media Thickness

8th Annual Congress of the European-Association-for-Haemophilia-and-Allied-Disorders -- FEB 11-13, 2015 -- Helsinki, FINLANDWOS: 000348680100033European Assoc Haemophilia & Allied Disorder