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A new perspective on the competitiveness of nations

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Author Info

  • Önsel, Sule
  • Ülengin, Füsun
  • Ulusoy, Gündüz
  • Aktas, Emel
  • Kabak, Özgür
  • Topcu, Y. Ilker
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    Abstract

    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Socio-Economic Planning Sciences.

    Volume (Year): 42 (2008)
    Issue (Month): 4 (December)
    Pages: 221-246

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    Handle: RePEc:eee:soceps:v:42:y:2008:i:4:p:221-246

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    Web page: http://www.elsevier.com/locate/seps

    Related research

    Keywords: Ranking Competitiveness Artificial neural network Cluster analysis;

    References

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    1. Oral, Muhittin & Cinar, Unver & Chabchoub, Habib, 1999. "Linking industrial competitiveness and productivity at the firm level," European Journal of Operational Research, Elsevier, vol. 118(2), pages 271-277, October.
    2. Hwarng, H. Brian & Ang, H. T., 2001. "A simple neural network for ARMA(p,q) time series," Omega, Elsevier, vol. 29(4), pages 319-333, August.
    3. Zanakis, Stelios H. & Becerra-Fernandez, Irma, 2005. "Competitiveness of nations: A knowledge discovery examination," European Journal of Operational Research, Elsevier, vol. 166(1), pages 185-211, October.
    4. Oral, Muhittin, 1993. "A methodology for competitiveness analysis and strategy formulation in glass industry," European Journal of Operational Research, Elsevier, vol. 68(1), pages 9-22, July.
    5. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
    6. Oral, Muhittin & Kettani, Ossama & Cosset, Jean-Claude & Daouas, Mohamed, 1992. "An estimation model for country risk rating," International Journal of Forecasting, Elsevier, vol. 8(4), pages 583-593, December.
    7. Ulengin, Fusun & Ulengin, Burc & Onsel, Sule, 2002. "A power-based measurement approach to specify macroeconomic competitiveness of countries," Socio-Economic Planning Sciences, Elsevier, vol. 36(3), pages 203-226, September.
    8. Oral, Muhittin & Chabchoub, Habib, 1997. "An estimation model for replicating the rankings of the world competitiveness report," International Journal of Forecasting, Elsevier, vol. 13(4), pages 527-537, December.
    9. Oral, Muhittin & Chabchoub, Habib, 1996. "On the methodology of the World Competitiveness Report," European Journal of Operational Research, Elsevier, vol. 90(3), pages 514-535, May.
    10. Glenn Milligan, 1980. "An examination of the effect of six types of error perturbation on fifteen clustering algorithms," Psychometrika, Springer, vol. 45(3), pages 325-342, September.
    11. Nour, Mohamed A. & Madey, Gregory R., 1996. "Heuristic and optimization approaches to extending the Kohonen self organizing algorithm," European Journal of Operational Research, Elsevier, vol. 93(2), pages 428-448, September.
    12. Hruschka, Harald, 1993. "Determining market response functions by neural network modeling: A comparison to econometric techniques," European Journal of Operational Research, Elsevier, vol. 66(1), pages 27-35, April.
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    Cited by:
    1. Joanicjusz Nazarko & Marta Komuda & Elzbieta Szubzda, 2008. "The DEA method in public sector institutions efficiency analysis on the basis of higher education institutions," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 4, pages 89-105.

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