A new perspective on the competitiveness of nations
By Sule Önsel, Füsun Ülengin, Gündüz Ulusoy, Emel Aktas, Ö. Kabak, Y. Topcu. 2008
118 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Strategy and Management
View Paper: https://www.semanticscholar.org/paper/7e3ad6325d4fe5d5746bab1c56e9c7095c780036
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Citations in the corpus
- Comparative analysis of data mining methods for bankruptcy prediction (2012, methodology): “[25] usedneural networkmodels to generateweights of 178 criteria which were then used in a model to classify country Decision Support Systems 52 (2012) 464–473”
- THE IMPACT OF INNOVATION PERFORMANCE ON THE COMPETITIVENESS OF THE VISEGRAD 4 COUNTRIES (2018, background): “In their research, Önsel et al. (2008) examine the competitiveness of countries, while declaring the fact that the gross domestic product as an indicator has many limitations in terms of a correct grouping of countries via cluster analysis.”
- Application of DEA method in efficiency evaluation of public higher education institutions (2014, background): “However, the costs for maintaining the public higher education sector are increasingly difficult to bear even for rich countries’ budgets (Johnes 2006; Önsel et al. 2008).”