Cilt 5 Sayı 1 (2017): BUSINESS & MANAGEMENT STUDIES: AN INTERNATIONAL JOURNAL
Makaleler

AVRUPA BİRLİĞİ ÜLKELERİNİN ARAŞTIRMA VE GELİŞTİRME ETKİNLİKLERİNİN ANALİZİ

Yeliz EKİNCİ
İstanbul Bilgi Üniversitesi
Melis Almula KARADAYI
İstanbul Bilgi Üniversitesi

Yayınlanmış 2017-04-21

Anahtar Kelimeler

  • Veri Zarflama Analizi,
  • AB Ülkeleri,
  • Ar-Ge Etkinliği

Nasıl Atıf Yapılır

EKİNCİ, Y., & KARADAYI, M. A. (2017). AVRUPA BİRLİĞİ ÜLKELERİNİN ARAŞTIRMA VE GELİŞTİRME ETKİNLİKLERİNİN ANALİZİ. Business & Management Studies: An International Journal, 5(1), 1–19. https://doi.org/10.15295/bmij.v5i1.97

Özet

Ülkelerin Araştırma ve Geliştirme (Ar-Ge) faaliyetleri gelişmekte olan pazarda rekabet edebilmek adına büyük önem taşımaktadır. Bu önem yaygın olarak kabul edilmesine rağmen, Ar-Ge faaliyetlerinin etkinliği literatürde nadir olarak incelenmiştir. Bu nedenle, bu çalışma  Avrupa Birliği (AB) üyesi ülkelerin Ar-Ge verimliliğini incelemeyi amaçlamaktadır. Çalışma kapsamında, ülkeler arasındaki rekabetin  çok yüksek olduğu AB ülkeleri seçilmiştir, ayrıca bu ülkeler Ar-Ge faaliyetlerine ciddi miktarda kaynak ayırmaktadırlar. Veri Zarflama Analizi (VZA) göreceli etkinlik skorlarını ölçmek için kullanılmıştır. Sonrasında, AB ülkelerinin siyasi ve düzenleyici ortamının Ar-Ge verimliliği üzerindeki etkisi hipotez testleri ile analiz edilmiştir. Göreceli etkinlik skorları ve hipotez testlerinin sonuçları, sosyal politika düzenleyiciler için Ar-Ge faaliyetlerinin planlanması konusunda karar vermede değerli bilgiler vermektedir. Çalışmanın sonuçları ayrıca Türkiye gibi, AB’ye katılmak isteyen ülkelere fayda sağlayacaktır.

İndirmeler

İndirme verileri henüz mevcut değil.

Referanslar

  1. Agutter, A.J. (1995). The Linguistic Significance of Current British Slang. (Published doctoral dissertation) Edinburgh Univesity, United Kingdom.
  2. Alp, İ. & Sözen, A. (2014). Turkey's Performance of Energy Consumption: A Study Making a Comparison with the EU Member States. Energy Sources, Part B: Economics, Planning, and Policy, 9(1), 87-100. doi:10.1080/15567241003773218
  3. Altıntaş, H. & Mercan, M. (2015). The Relationship between Research and Development (R&D) Expenditures: Panel Cointegration Analysis Under Cross Sectional Dependency on OECD Countries. Ankara Üniversitesi SBF Dergisi, 70(2), 345-376.
  4. Aristovnik, A. (2012). The Relative Efficiency of Education and R&D Expenditures in the New EU Member States. Journal of Business Economics and Management, 13(5), 832-848. doi:10.3846/16111699.2011.620167
  5. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8
  6. Cook, W. D., Cooper, W. W.,M Seiford, L. M., & Tone, K. (2001). Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-solver Software. Kluwer Academic Publishers.
  7. Cullmann, A., Schmidt-ehmcke, J., & ZloczystI, P. (2009). Innovation, R&D Efficiency and the Impact of the Regulatory Environment: A Two-stage Semi-parametric DEA Approach. DIW Berlin Discussion Paper, No: 883.
  8. Doyle, J. & Green, R. (1994). Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses. Journal of the Operational Research Society, 45(5), 567-578. doi: 10.2307/2584392
  9. Dutta, S. & Lanvin, B. (2013), The Global Innovation Index 2013.
  10. EPO (2016). Retrived from https://www.epo.org/index.html
  11. Eurostat (2016). Retrived from http://ec.europa.eu/eurostat
  12. Farrell, M.J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, Series A,120, 253-290.doi: 10.2307/2343100
  13. Garcia-valderrama, T., Mulero-mendigorri, E., & Revuelta-bordoy, D. (2009). Relating the Perspectives of the Balanced Scorecard for R&D by Means of DEA. European Journal of Operational Research, 196(3), 1177-1189. doi:10.1016/j.ejor.2008.05.015
  14. Güngör, K. (2016). Compliance Of Candidate Countries’ Fiscal Performances To European Union Membership Criteria In Global Crisis Process/Küresel Ekonomik Kriz Sürecinde Aday Ülkelerin Mali Performanslarinin Avrupa Birliği’ne Üyelik Kriterlerine Uyumu. Business And Management Studies: An International Journal, 4(3), 273-290.
  15. Han, U., Asmild, M., & Kunc, M. (2014). Regional R&D Efficiency in Korea from Static and Dynamic Perspectives. Regional Studies, 1-15. doi:10.1080/00343404.2014.984670
  16. Holland, M. Guide to citing Internet sources (2004, November 4). Retrived from http://www.bournemouth.ac.uk/library/using/guide_to_citing_internet_sourc.html.
  17. Hu, J. L., Yang, C. H., & Chen, C. P. (2014). R&D Efficiency and the National Innovation System: An International Comparison Using the Distance Function Approach. Bulletin of Economic Research, 66(1), 55-71.doi: 10.1111/j.1467-8586.2011.00417.x
  18. Lee, H. Y. & Park, Y. T. (2005). An International Comparison of R&D Efficiency: DEA Approach. Asian Journal of Technology Innovation. 13(2), 207-222. doi: 10.1080/19761597.2005.9668614
  19. Lee, H., Park, Y. & Choi, H. (2009). Comparative Evaluation of Performance of National R&D Programs with Heterogeneous Objectives: A DEA Approach. European Journal of Operational Research, 196(3), 847-855. doi:10.1016/j.ejor.2008.06.016
  20. Lee, K. & Yoon, B. (2015). The Idiosyncrasy of Research and Development Efficiency Across Types of Small‐and medium‐sized Enterprises: Evidence from Korea. R&D Management, 45(3), 250-266. doi:10.1111/radm.12082.
  21. Luenberger, D.G. (1973). Introduction to Linear and Nonlinear Programming. Addison-Wesley: California.
  22. Roman, M. (2010). Regional Efficiency of the Knowledge Economy in the New EU Countries: The Romanian and Bulgarian Case. Romanian Journal of Regional Science, 4(1), 33-53.
  23. Rosen, J.B. (1961). The Gradient Projection Method for Nonlinear Programming: Part II Nonlinear Constraints. Journal of the Society for Industrial and Applied Mathematics, 9, 514-532. doi:10.1137/0109044.
  24. Sharma, S., & Thomas, V. (2008). Inter-country R&D efficiency analysis: An application of data envelopment analysis. Scientometrics, 76(3), 483-501.
  25. Sherman, H. D. & Zhu, J. (2006), Service Productivity Management: Improving Service Performance Using Data Envelopment Analysis (DEA). Springer Science & Business Media: Boston, MA, USA.
  26. Thomas, V. J., Jain, S. K. & Sharma, S. (2009). Analyzing R&D Efficiency in Asia and the OECD: An Application of the Malmquist Productivity Index. In Science and Innovation Policy, 2009 Atlanta Conference on IEEE, 1-10.
  27. Thomas, V., J. Sharma, S. & Jain, S. K. (2011). Using Patents and Publications to Assess R&D Efficiency in the States of the USA. World Patent Information, 33(1), 4-10.doi:10.1016/j.wpi.2010.01.005
  28. Ulucan,A. (2002). Data Envelopment Analysis Approach in Efficiency Measurement of IS0500 Companies: Evaluations Using Different Input Output Components and Different Returns to Scale. Ankara Üniversitesi SBF Dergisi, 57(2), 187-202.
  29. USPTO. (2016). Retrived from http://www.uspto.gov
  30. Wang, E. C. (2007). R&D Efficiency and Economic Performance: A Cross-country Analysis Using the Stochastic Frontier Approach. Journal of Policy Modeling, 29(2), 345-360. doi:10.1016/j.jpolmod.2006.12.005.
  31. Wu, H. C., & Liu, S. F. (2007). Evaluaton on the R&D Relative Efficiency of Different Areas in China Based on Improved DEA Model [J]. R&D Management, 2, 108-112.
  32. Yıldız, A. (2006). Evaluation of Portfolio Performance with Data Envelopment. Ankara Üniversitesi SBF Dergisi, 61(2), 211-234