Araştırma Makalesi
Yıl 2016,
Cilt: 8 Sayı: 3, 107 - 114, 31.07.2016
Öz
The content of the economic development in recent years has undergone a change towards competitiveness and innovation due to the technological advances and globalization. These changes, particularly in developing countries, have positive impacts such as significantly increase in productivity and ease of access to new markets. In this paper, a methodology was proposed by using fuzzy c-means clustering algorithm in order to classify countries based on their technology use and innovation indicators. According to the numerical application carried out for 52 developing countries, it was determined that proposed method gave remarkable results.
Anahtar Kelimeler
Kaynakça
- Ahire, S. L., Ravichandran, T. (2001). An innovation diffusion model of TQM implementation. IEEE transactions on engineering management, 48(4), 445-464. Arvanitis, S., Hollenstein, H. (1998). Innovative activity and firm characteristics–a cluster analysis with firm-level data of Swiss manufacturing. In 25th Annual EARIE Conference, Copenhagen (pp. 27-30). Bezdek, J. C., Pal, S. K. (1992). Fuzzy Models for Pattern Recognition: Methods that Search for Structure in Data. New York: IEEE Press. Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media. Celikyilmaz, A., Türkşen, I.B. (2008). Enhanced fuzzy system models with improved fuzzy clustering algorithm. IEEE Trans. Fuzzy Syst., 16(3), 779-794. Celikyilmaz, A., Türksen, I. B. (2009). Modeling Uncertainty with Fuzzy Logic: With Recent Theory and Applications. Berlin Heidelberg: Springer-Verlag. Davó, N. B., Mayor, M. G. O., de la Hera, M. L. B. (2011). Empirical analysis of technological innovation capacity and competitiveness in EU-15 countries. African journal of business management, 5(14), 5753-5765. De Oliveira, J.V., Pedrycz, W. (2007). Advances in fuzzy clustering and its applications. New York: Wiley. BAŞER, ÖZCAN, TÜRE / Assessment of Developing Countries Based on Their Level of… Sayfa | 114 Galende, J. (2006). Analysis of technological innovation from business economics and management. Technovation, 26(3), 300-311. Grupp, H., Mogee, M. E. (2004). Indicators for national science and technology policy: how robust are composite indicators?.Research Policy, 33(9), 1373-1384. Guan, J. C., Yam, R. C., Mok, C. K., Ma, N. (2006). A study of the relationship between competitiveness and technological innovation capability based on DEA models. European Journal of Operational Research, 170(3), 971-986. Hall, B. H., Lerner, J. (2010). The financing of R&D and innovation. Handbook of the Economics of Innovation, 1, 609-639. Hammah, R. E., Curran, J. H. (1998). Fuzzy cluster algorithm for the automatic identification of joint sets. International Journal of Rock Mechanics and Mining Sciences, 35(7), 889-905. Höppner, F., Klawonn, F., Kruse, R., Runkler, T. (1999). Fuzzy cluster analysis: methods for classification, data analysis and image recognition. John Wiley & Sons. Juma, C., Fang, K., Honca, D., Huete-Perez, J., Konde, V., Lee, S. H., Arenas, J., Ivinson, A., Robinson, H., Singh, S. (2001). Global governance of technology: meeting the needs of developing countries. International Journal of Technology Management, 22(7-8), 629-655. Kim, M., Ramakrishna, R. S. (2005). New indices for cluster validity assessment. Pattern Recognition Letters 26, 2353-2363. Lu, F., Jiao, K. (2008). Cluster Analysis on Technological Innovation Ability of Enterprises Based on SOM Network: Taking the MLEs in Henan as an Example. In 2008 ISECS International Colloquium on Computing, Communication, Control, and Management (Vol. 3, pp. 230-234). IEEE. Mielgo, N., Montes-Peón, J. M., Vázquez-Ordás, C. J. (2009). Are quality and innovation management conflicting activities?. Technovation,29(8), 537-545. Nasierowski, W., Arcelus, F. J. (1999). Interrelationships among the elements of national innovation systems: A statistical evaluation. European Journal of Operational Research, 119(2), 235-253. Nasierowski, W., &Arcelus, F. J. (2003). On the efficiency of national innovation systems. Socio-Economic Planning Sciences, 37(3), 215-234. Nayak, J., Naik, B., Behera, H. S. (2015). Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014. In Computational Intelligence in Data Mining-Volume 2 (pp. 133-149). Springer India. Nefti, S., Oussalah, M. (2004). Probabilistic-fuzzy clustering algorithm. In Systems, Man and Cybernetics, 2004 IEEE International Conference on (Vol. 5, pp. 4786-4791). IEEE. OECD (2005). Oslo Manual—Guidelines for Collecting and Interpreting Innovation Data, Paris. Ostaszewski, K. M. (1993). An investigation into possible applications of fuzzy set methods in actuarial science. Schaumburg, Illinois: Society of Actuaries. Pal, N. R., Bezdek, J. C. (1995). On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy Systems, 3(3), 370-379. Porter, M. E. (1991). Towards a dynamic theory of strategy. Strategic management journal, 12, 95-117. Rosenberg, N. (1994). Exploring the black box: Technology, economics, and history. Cambridge: Cambridge University Press. Tirole, J. (1995). The Theory of Industrial Organization. MIT press. Williams, L. K., McGuire, S. J. (2010). Economic creativity and innovation implementation: the entrepreneurial drivers of growth? Evidence from 63 countries. Small Business Economics, 34(4), 391-412. Xie, X. L., Beni, G. A. (1991). Validity Measure for Fuzzy Clustering. IEEE Trans. Pattern and Machine Intelligence, 3(8), 841-846. Zhang, Y. J. (1996). A survey on evaluation methods for image segmentation. Pattern recognition, 29(8), 1335-1346.
Yıl 2016,
Cilt: 8 Sayı: 3, 107 - 114, 31.07.2016
Öz
The content of the economic development in recent years has undergone a change towards competitiveness and innovation due to the technological advances and globalization. These changes, particularly in developing countries, have positive impacts such as significantly increase in productivity and ease of access to new markets. In this paper, a methodology was proposed by using fuzzy c-means clustering algorithm in order to classify countries based on their technology use and innovation indicators. According to the numerical application carried out for 52 developing countries, it was determined that proposed method gave remarkable results.
Anahtar Kelimeler
Kaynakça
- Ahire, S. L., Ravichandran, T. (2001). An innovation diffusion model of TQM implementation. IEEE transactions on engineering management, 48(4), 445-464. Arvanitis, S., Hollenstein, H. (1998). Innovative activity and firm characteristics–a cluster analysis with firm-level data of Swiss manufacturing. In 25th Annual EARIE Conference, Copenhagen (pp. 27-30). Bezdek, J. C., Pal, S. K. (1992). Fuzzy Models for Pattern Recognition: Methods that Search for Structure in Data. New York: IEEE Press. Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media. Celikyilmaz, A., Türkşen, I.B. (2008). Enhanced fuzzy system models with improved fuzzy clustering algorithm. IEEE Trans. Fuzzy Syst., 16(3), 779-794. Celikyilmaz, A., Türksen, I. B. (2009). Modeling Uncertainty with Fuzzy Logic: With Recent Theory and Applications. Berlin Heidelberg: Springer-Verlag. Davó, N. B., Mayor, M. G. O., de la Hera, M. L. B. (2011). Empirical analysis of technological innovation capacity and competitiveness in EU-15 countries. African journal of business management, 5(14), 5753-5765. De Oliveira, J.V., Pedrycz, W. (2007). Advances in fuzzy clustering and its applications. New York: Wiley. BAŞER, ÖZCAN, TÜRE / Assessment of Developing Countries Based on Their Level of… Sayfa | 114 Galende, J. (2006). Analysis of technological innovation from business economics and management. Technovation, 26(3), 300-311. Grupp, H., Mogee, M. E. (2004). Indicators for national science and technology policy: how robust are composite indicators?.Research Policy, 33(9), 1373-1384. Guan, J. C., Yam, R. C., Mok, C. K., Ma, N. (2006). A study of the relationship between competitiveness and technological innovation capability based on DEA models. European Journal of Operational Research, 170(3), 971-986. Hall, B. H., Lerner, J. (2010). The financing of R&D and innovation. Handbook of the Economics of Innovation, 1, 609-639. Hammah, R. E., Curran, J. H. (1998). Fuzzy cluster algorithm for the automatic identification of joint sets. International Journal of Rock Mechanics and Mining Sciences, 35(7), 889-905. Höppner, F., Klawonn, F., Kruse, R., Runkler, T. (1999). Fuzzy cluster analysis: methods for classification, data analysis and image recognition. John Wiley & Sons. Juma, C., Fang, K., Honca, D., Huete-Perez, J., Konde, V., Lee, S. H., Arenas, J., Ivinson, A., Robinson, H., Singh, S. (2001). Global governance of technology: meeting the needs of developing countries. International Journal of Technology Management, 22(7-8), 629-655. Kim, M., Ramakrishna, R. S. (2005). New indices for cluster validity assessment. Pattern Recognition Letters 26, 2353-2363. Lu, F., Jiao, K. (2008). Cluster Analysis on Technological Innovation Ability of Enterprises Based on SOM Network: Taking the MLEs in Henan as an Example. In 2008 ISECS International Colloquium on Computing, Communication, Control, and Management (Vol. 3, pp. 230-234). IEEE. Mielgo, N., Montes-Peón, J. M., Vázquez-Ordás, C. J. (2009). Are quality and innovation management conflicting activities?. Technovation,29(8), 537-545. Nasierowski, W., Arcelus, F. J. (1999). Interrelationships among the elements of national innovation systems: A statistical evaluation. European Journal of Operational Research, 119(2), 235-253. Nasierowski, W., &Arcelus, F. J. (2003). On the efficiency of national innovation systems. Socio-Economic Planning Sciences, 37(3), 215-234. Nayak, J., Naik, B., Behera, H. S. (2015). Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014. In Computational Intelligence in Data Mining-Volume 2 (pp. 133-149). Springer India. Nefti, S., Oussalah, M. (2004). Probabilistic-fuzzy clustering algorithm. In Systems, Man and Cybernetics, 2004 IEEE International Conference on (Vol. 5, pp. 4786-4791). IEEE. OECD (2005). Oslo Manual—Guidelines for Collecting and Interpreting Innovation Data, Paris. Ostaszewski, K. M. (1993). An investigation into possible applications of fuzzy set methods in actuarial science. Schaumburg, Illinois: Society of Actuaries. Pal, N. R., Bezdek, J. C. (1995). On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy Systems, 3(3), 370-379. Porter, M. E. (1991). Towards a dynamic theory of strategy. Strategic management journal, 12, 95-117. Rosenberg, N. (1994). Exploring the black box: Technology, economics, and history. Cambridge: Cambridge University Press. Tirole, J. (1995). The Theory of Industrial Organization. MIT press. Williams, L. K., McGuire, S. J. (2010). Economic creativity and innovation implementation: the entrepreneurial drivers of growth? Evidence from 63 countries. Small Business Economics, 34(4), 391-412. Xie, X. L., Beni, G. A. (1991). Validity Measure for Fuzzy Clustering. IEEE Trans. Pattern and Machine Intelligence, 3(8), 841-846. Zhang, Y. J. (1996). A survey on evaluation methods for image segmentation. Pattern recognition, 29(8), 1335-1346.
Toplam 1 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | İşletme |
| Bölüm | Derleme Makale |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Temmuz 2016 |
| Yayımlandığı Sayı | Yıl 2016Cilt: 8 Sayı: 3 |
