Research Article
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Differences in Efficiency of Innovation Performance among Middle Income Countries: An Empirical Approach

Year 2019, Volume: 5 Issue: 2, 213 - 229, 24.10.2019
https://doi.org/10.20979/ueyd.567962

Abstract

The purpose of this paper is to determine empirically the differences in the
efficiency of innovation performance in middle-income countries. To achieve
this aim, it was used cluster analysis which is one of the multivariate
statistical techniques. Ward’s agglomerative hierarchical method was employed
for cluster analysis. In determining efficiency of innovation performance, it
was followed process suggested by Kula and Ünlü (2019). So, cluster analysis
was performed separately for inputs and outputs indicators. Secondly,
discriminant analysis was used to identify factors that lead to differences in
the efficiency. According to the World Bank's income classification, it was
included
a total of
54 countries, including 23
lower-middle
income
and 31 upper-middle income. The data used in the analysis was obtained from Global Innovation Index. The
findings confirm the existence of the
inefficiency problem in terms of innovation
performance in the middle income countries. 

References

  • Agresti, A. (1996). An Introduction to Categorical Data Analysis. USA: John Wiley and Sons Ltd.
  • Akın, H. B. ve Eren, Ö. (2012). OECD Ülkelerinin Eğitim Göstergelerinin Kümeleme Analizi ve Çok Boyutlu Ölçekleme Analizi ile Karşılaştırmalı Analizi, Öneri Dergisi, 10 (37), 175-181.
  • Altınel, F. (2012). An Empirical Study on Fuzzy C-Means Clustering for Turkish Banking System, The Graduate School of Social Sciences of Middle East Technical University, Ankara.
  • Arı, E. and Yıldız, A. (2018), OECD Ülkelerinin Göç İstatistikleri Bakımından Bulanık Kümeleme Analizi ile İncelenmesi, Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 33, 17-28.
  • Artis, M.J. and Zhang, W. (2002). Membership of EMU: A Fuzzy Clustering Analysis of Alternative Criteria, Journal of Economic Integration, 17(1), 54-79.
  • Atik, H. and Ünlü, F. (2017). Science Performance of Turkey in 21St Century: A Multivariate Statistical Comparison with the OECD Countries, In: Researches on Science and Art in 21st Century Turkey, Arapgirlioğlu H., Atik A., Elliot R. L., Turgeon E. (Eds.), Gece Publishing, Ankara,1030-1038.
  • Baculakova, K. and Gress, M. (2015). Cluster Analysis of Creative Industries in the EU, Economic Annals-XXI, 9-10, 15-18.
  • Barasa, L.; Vermeulen, P.; Knoben, J.; Kinyanjui, B. and Kimuyu, P. (2019). Innovation inputs and efficiency: manufacturing firms in Sub-Saharan Africa, European Journal of Innovation Management, 22 (1), 59-83.
  • Bivand, R.S.; Wilk, J. and Kossowski, T. (2017). Spatial association of population pyramids across Europe: The application of symbolic data, cluster analysis and join-count tests, Spatial Statistics, 21, 339–361.
  • Broekel, T.; Rogge, N. and Brenner, T. (2013). The innovation efficiency of German regions-a shared-input DEA approach, Working Papers on Innovation and Space Philipps-Universität Marburg.
  • Cai, Y. (2011). Factors Affecting the Efficiency of the BRICSs’ National Innovation Systems: A Comparative Study based on DEA and Panel Data Analysis, Economics Discussion Paper No. 2011-52.
  • Chou, J. and Gao, F. (2013). Innovation Efficiency, Global Diversification, and Firm Value, http://cafd.cufe.edu.cn/docs/2013-05/20130527101741442337.pdf, (Access: 15.01.2019)
  • Christensen, I. A., K. Davidian, D. Kaiser and J. Foust. (2010). Applying Disruptive Innovation Theory in Emerging Markets for Crew On-Orbit Transportation, https://swfound.org/media/199710/ic_iac_sep2010.pdf (Access: 15.11.2015)
  • Çiçek, H. and Onat, O. K. (2012), İnovasyon Odaklı Faaliyetlerin Firma Performansına Etkisinin Veri Zarflama Analizi ile Belirlenmesi; İMKB Üzerine Bir Araştırma, Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 4 (7), 46-53.
  • Egawa, A. (2013). Will Income Inequality Cause a Middle Income Trap in Asia?, Bruegel Working Paper, No: 2013/06.
  • Erkekoğlu, Hatice ve H. Kıvanç Arıç; (2013), “APEC Ülkeleri ve Türki-ye’nin Bilgi Toplumu Kriterleri Açısından İstatistiksel Analizi ve Bazı Tespitler”, Bilgi Ekonomisi ve Yönetimi Dergisi, 8 (1), 103-114.
  • Ersöz, F. (2009). Avrupa İnovasyon Göstergeleri (EIS) Işığında Türkiye’nin Konumu, İTÜ Dergisi/b Sosyal Bilimler, 6 (1), 3-16.
  • Everitt, B.S.; Landau, S.; Leese, M. and Sathal, D. (2011). Cluster Analysis, Fifth Edition, UK: John Wiley & Sons, Ltd.
  • Foreman-Peck, J. (2012). Effectiveness and efficiency of SME innovation policy, Cardiff Economics Working Papers, No. E2012/4, Cardiff University, Cardiff Business School, Cardiff.
  • Gill, I. and H. Kharas. (2007). An East Asian Renaissance: Ideas for Economic Growth, Washington: World Bank Publications.
  • Hajek, P. and Henriques, R. (2017). Modelling innovation performance of European regions using multi-output neural networks, PLOS One, 12 (10), 1-21.
  • Henderson, R. M. and K. B. Clark. (1990). Architectural Innovation: The Reconfiguration Of Existing, Administrative Science Quarterly, 35 (1), 9-30.
  • Herimalala, R. and Gausesns, O. (2012). X-Efficiency of Innovation Processes: Concept and Evaluation based on Data Envelopment Analysis, MPRA Paper No. 42872.
  • Jankowska, A., A. J. Nagengast and J. R. Perea. (2012). The Middle Income Trap: Comparing Asian and Latin American Experiences, OECD Development Centre Policy Insights, No: 96.
  • Kula, F.; Ünlü, F. (2019). Ecological Innovation Efforts and Performances: an Empirical Analysis, In: Energy and Environmental Strategies in the Era of Globalization, Shahbaz, M.; Balsalobre, D. Eds. Switzerland: Springer (in press).
  • Nakip, M. (2006). Pazarlama Araştırmaları Teknikler ve (SPSS Destekli) Uygulamalar. Genişletilmiş İkinci Baskı, Ankara: Seçkin Yayıncılık.
  • Nasierowski, W. and Arcelus, F. J. (2012). About Efficiency of Innovations: What Can Be Learned From The Innovation Union Scoreboard Index, 8th International Strategic Management Conference Procedia - Social and Behavioral Sciences, 58, 792–801.
  • OECD-Eurostat (2005). Oslo Kılavuzu: Yenilik Verilerinin Toplanması ve Yorumlanması İçin İlkeler, Üçüncü Baskı, Ankara: TÜBİTAK Yayınları.
  • Özbek, H. and Atik, A. (2013). İnovasyon Göstergeleri Bakımından Türkiye’nin Avrupa Birliği Ülkeleri Arasındaki Yeri: İstatistiksel Bir Analiz, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 42: 193-210.
  • Popescu, M.E.; Cristescu, A. and Stanila, A. (2016). Net earnings trends in the EU countries, Theoretical and Applied Economics, XXIII, No. 3(608), 351-360.
  • Porter, M. E. (1991). The Competitive Advantage of Nations, New York: MacMillan Press.
  • Roszko-Wojtowicz, E. and Bialek, J. (2017). Evaluation of the EU Countries’ Innovative Potential–Multivariate Approach, Statistics in Transition New Series, 18 (1), 167–180.
  • Saatçioğlu, C. and Bildirici, Ü. (2017). İnovasyon Göstergeleri Bakımından Türkiye’nin OECD Ülkeleri Arasındaki Yeri: Ekonometrik Bir Uygulama, İşletme ve İktisat Çalışmaları Dergisi, 5 (4), 44-56.
  • Sarstedt, M. and Mooi, E. (2014). Cluster Analysis. In: A Concise Guide to Market Research. Springer Texts in Business and Economics, Sarstedt, M.; Mooi, E. Eds.; Springer: Berlin, 273-324.
  • Schmidt, T. and Rammer, C. (2007). Non-technological and Technological Innovation: Strange Bedfellows?, Centre for European Economic Research Discussion Paper No. 07-052.
  • Schumpeter, J. A. (1934). The Theory of Economic Development, New Jersey: Transaction Publishers.
  • Strozek, P. (2012). Comparative Analysis of the Level of Knowledge-based Part of Economies in European Union Countries with KAM Methodology, Comparative Economic Research, 15 (4), 249-263.
  • The World Bank (2019). Country Classifications, https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (Access: 06.04.2019)
  • Trott, P. (2005). Innovation Management and New Product Development (Fifth Edition), UK: Pearson Education Limited.
  • Verma, J.P. (2013). Data Analysis in Management with SPSS Software. India: Springer.
  • Ward, J.H. (1963). Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, 58, 236–244.
  • WIPO (2018). The Global Innovation Index 2018, Geneva, Switzerland.
  • Yeldan, E. (2012). Türkiye Orta Gelir Tuzağına Yaklaşırken, İktisat ve Toplum Dergisi, 21-22, 26-30.
  • Yeloğlu, H.O. (2009). Bilgi Ekonomisi Değişkenlerine Yönelik İlk İzlenimler: Türkiye OECD Ülkeleri Karşılaştırmaları (1995-1999), Bilgi Dünyası, 10 (2), 245-260.
  • Yılmaz, Y. K.; Yılmaz, M.; Yiğitbaşı, M.E. and Çoban, O. (2016). İnovasyon İndeksi Yardımıyla Türkiye’de İllerin Rekabetçilik Analizi: Düzey-III Örneği, Sosyoekonomi, 24(30), 71-90.

Differences in Efficiency of Innovation Performance among Middle Income Countries: An Empirical Approach

Year 2019, Volume: 5 Issue: 2, 213 - 229, 24.10.2019
https://doi.org/10.20979/ueyd.567962

Abstract

Bu çalışmanın amacı, orta gelirli ülkelerde inovasyon performansının
etkinliğindeki farklılıkları ampirik olarak tespit etmektir. Bu amaca ulaşmak
için, çok değişkenli istatistiksel tekniklerden kümeleme analizi
kullanılmıştır. Bu analiz için Ward’ın aglomeratif hiyerarşik yöntemi
kullanılmıştır. İnovasyon performansının etkinliğini belirlerken, Kula ve Ünlü
(2019) tarafından önerilen süreç takip edilmiştir. Böylece girdi ve çıktı
göstergeleri için analizler ayrı ayrı yapılmıştır. İkinci olarak, etkinlikte
farklılıklara yol açan faktörleri belirlemek için diskriminant analizi
kullanılmıştır. Dünya Bankası'nın gelir sınıflandırmasına göre, 23 alt-orta
gelirli ve 31 üst-orta gelirli ülke l olmak üzere toplam 54 ülke analize dahil
edilmiştir. Analizde kullanılan veriler Global İnovasyon Endeksinden elde
edilmiştir. Bulgular orta gelirli ülkerlerde inovasyon performansı açısından etkinlik
sorununun varlığını doğrulamaktadır.

References

  • Agresti, A. (1996). An Introduction to Categorical Data Analysis. USA: John Wiley and Sons Ltd.
  • Akın, H. B. ve Eren, Ö. (2012). OECD Ülkelerinin Eğitim Göstergelerinin Kümeleme Analizi ve Çok Boyutlu Ölçekleme Analizi ile Karşılaştırmalı Analizi, Öneri Dergisi, 10 (37), 175-181.
  • Altınel, F. (2012). An Empirical Study on Fuzzy C-Means Clustering for Turkish Banking System, The Graduate School of Social Sciences of Middle East Technical University, Ankara.
  • Arı, E. and Yıldız, A. (2018), OECD Ülkelerinin Göç İstatistikleri Bakımından Bulanık Kümeleme Analizi ile İncelenmesi, Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 33, 17-28.
  • Artis, M.J. and Zhang, W. (2002). Membership of EMU: A Fuzzy Clustering Analysis of Alternative Criteria, Journal of Economic Integration, 17(1), 54-79.
  • Atik, H. and Ünlü, F. (2017). Science Performance of Turkey in 21St Century: A Multivariate Statistical Comparison with the OECD Countries, In: Researches on Science and Art in 21st Century Turkey, Arapgirlioğlu H., Atik A., Elliot R. L., Turgeon E. (Eds.), Gece Publishing, Ankara,1030-1038.
  • Baculakova, K. and Gress, M. (2015). Cluster Analysis of Creative Industries in the EU, Economic Annals-XXI, 9-10, 15-18.
  • Barasa, L.; Vermeulen, P.; Knoben, J.; Kinyanjui, B. and Kimuyu, P. (2019). Innovation inputs and efficiency: manufacturing firms in Sub-Saharan Africa, European Journal of Innovation Management, 22 (1), 59-83.
  • Bivand, R.S.; Wilk, J. and Kossowski, T. (2017). Spatial association of population pyramids across Europe: The application of symbolic data, cluster analysis and join-count tests, Spatial Statistics, 21, 339–361.
  • Broekel, T.; Rogge, N. and Brenner, T. (2013). The innovation efficiency of German regions-a shared-input DEA approach, Working Papers on Innovation and Space Philipps-Universität Marburg.
  • Cai, Y. (2011). Factors Affecting the Efficiency of the BRICSs’ National Innovation Systems: A Comparative Study based on DEA and Panel Data Analysis, Economics Discussion Paper No. 2011-52.
  • Chou, J. and Gao, F. (2013). Innovation Efficiency, Global Diversification, and Firm Value, http://cafd.cufe.edu.cn/docs/2013-05/20130527101741442337.pdf, (Access: 15.01.2019)
  • Christensen, I. A., K. Davidian, D. Kaiser and J. Foust. (2010). Applying Disruptive Innovation Theory in Emerging Markets for Crew On-Orbit Transportation, https://swfound.org/media/199710/ic_iac_sep2010.pdf (Access: 15.11.2015)
  • Çiçek, H. and Onat, O. K. (2012), İnovasyon Odaklı Faaliyetlerin Firma Performansına Etkisinin Veri Zarflama Analizi ile Belirlenmesi; İMKB Üzerine Bir Araştırma, Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 4 (7), 46-53.
  • Egawa, A. (2013). Will Income Inequality Cause a Middle Income Trap in Asia?, Bruegel Working Paper, No: 2013/06.
  • Erkekoğlu, Hatice ve H. Kıvanç Arıç; (2013), “APEC Ülkeleri ve Türki-ye’nin Bilgi Toplumu Kriterleri Açısından İstatistiksel Analizi ve Bazı Tespitler”, Bilgi Ekonomisi ve Yönetimi Dergisi, 8 (1), 103-114.
  • Ersöz, F. (2009). Avrupa İnovasyon Göstergeleri (EIS) Işığında Türkiye’nin Konumu, İTÜ Dergisi/b Sosyal Bilimler, 6 (1), 3-16.
  • Everitt, B.S.; Landau, S.; Leese, M. and Sathal, D. (2011). Cluster Analysis, Fifth Edition, UK: John Wiley & Sons, Ltd.
  • Foreman-Peck, J. (2012). Effectiveness and efficiency of SME innovation policy, Cardiff Economics Working Papers, No. E2012/4, Cardiff University, Cardiff Business School, Cardiff.
  • Gill, I. and H. Kharas. (2007). An East Asian Renaissance: Ideas for Economic Growth, Washington: World Bank Publications.
  • Hajek, P. and Henriques, R. (2017). Modelling innovation performance of European regions using multi-output neural networks, PLOS One, 12 (10), 1-21.
  • Henderson, R. M. and K. B. Clark. (1990). Architectural Innovation: The Reconfiguration Of Existing, Administrative Science Quarterly, 35 (1), 9-30.
  • Herimalala, R. and Gausesns, O. (2012). X-Efficiency of Innovation Processes: Concept and Evaluation based on Data Envelopment Analysis, MPRA Paper No. 42872.
  • Jankowska, A., A. J. Nagengast and J. R. Perea. (2012). The Middle Income Trap: Comparing Asian and Latin American Experiences, OECD Development Centre Policy Insights, No: 96.
  • Kula, F.; Ünlü, F. (2019). Ecological Innovation Efforts and Performances: an Empirical Analysis, In: Energy and Environmental Strategies in the Era of Globalization, Shahbaz, M.; Balsalobre, D. Eds. Switzerland: Springer (in press).
  • Nakip, M. (2006). Pazarlama Araştırmaları Teknikler ve (SPSS Destekli) Uygulamalar. Genişletilmiş İkinci Baskı, Ankara: Seçkin Yayıncılık.
  • Nasierowski, W. and Arcelus, F. J. (2012). About Efficiency of Innovations: What Can Be Learned From The Innovation Union Scoreboard Index, 8th International Strategic Management Conference Procedia - Social and Behavioral Sciences, 58, 792–801.
  • OECD-Eurostat (2005). Oslo Kılavuzu: Yenilik Verilerinin Toplanması ve Yorumlanması İçin İlkeler, Üçüncü Baskı, Ankara: TÜBİTAK Yayınları.
  • Özbek, H. and Atik, A. (2013). İnovasyon Göstergeleri Bakımından Türkiye’nin Avrupa Birliği Ülkeleri Arasındaki Yeri: İstatistiksel Bir Analiz, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 42: 193-210.
  • Popescu, M.E.; Cristescu, A. and Stanila, A. (2016). Net earnings trends in the EU countries, Theoretical and Applied Economics, XXIII, No. 3(608), 351-360.
  • Porter, M. E. (1991). The Competitive Advantage of Nations, New York: MacMillan Press.
  • Roszko-Wojtowicz, E. and Bialek, J. (2017). Evaluation of the EU Countries’ Innovative Potential–Multivariate Approach, Statistics in Transition New Series, 18 (1), 167–180.
  • Saatçioğlu, C. and Bildirici, Ü. (2017). İnovasyon Göstergeleri Bakımından Türkiye’nin OECD Ülkeleri Arasındaki Yeri: Ekonometrik Bir Uygulama, İşletme ve İktisat Çalışmaları Dergisi, 5 (4), 44-56.
  • Sarstedt, M. and Mooi, E. (2014). Cluster Analysis. In: A Concise Guide to Market Research. Springer Texts in Business and Economics, Sarstedt, M.; Mooi, E. Eds.; Springer: Berlin, 273-324.
  • Schmidt, T. and Rammer, C. (2007). Non-technological and Technological Innovation: Strange Bedfellows?, Centre for European Economic Research Discussion Paper No. 07-052.
  • Schumpeter, J. A. (1934). The Theory of Economic Development, New Jersey: Transaction Publishers.
  • Strozek, P. (2012). Comparative Analysis of the Level of Knowledge-based Part of Economies in European Union Countries with KAM Methodology, Comparative Economic Research, 15 (4), 249-263.
  • The World Bank (2019). Country Classifications, https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (Access: 06.04.2019)
  • Trott, P. (2005). Innovation Management and New Product Development (Fifth Edition), UK: Pearson Education Limited.
  • Verma, J.P. (2013). Data Analysis in Management with SPSS Software. India: Springer.
  • Ward, J.H. (1963). Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, 58, 236–244.
  • WIPO (2018). The Global Innovation Index 2018, Geneva, Switzerland.
  • Yeldan, E. (2012). Türkiye Orta Gelir Tuzağına Yaklaşırken, İktisat ve Toplum Dergisi, 21-22, 26-30.
  • Yeloğlu, H.O. (2009). Bilgi Ekonomisi Değişkenlerine Yönelik İlk İzlenimler: Türkiye OECD Ülkeleri Karşılaştırmaları (1995-1999), Bilgi Dünyası, 10 (2), 245-260.
  • Yılmaz, Y. K.; Yılmaz, M.; Yiğitbaşı, M.E. and Çoban, O. (2016). İnovasyon İndeksi Yardımıyla Türkiye’de İllerin Rekabetçilik Analizi: Düzey-III Örneği, Sosyoekonomi, 24(30), 71-90.
There are 45 citations in total.

Details

Primary Language English
Subjects Economics
Journal Section Research Articles
Authors

Fatma Ünlü 0000-0003-1822-9965

Publication Date October 24, 2019
Submission Date May 20, 2019
Published in Issue Year 2019 Volume: 5 Issue: 2

Cite

APA Ünlü, F. (2019). Differences in Efficiency of Innovation Performance among Middle Income Countries: An Empirical Approach. Uluslararası Ekonomi Ve Yenilik Dergisi, 5(2), 213-229. https://doi.org/10.20979/ueyd.567962

International Journal of Economics and Innovation

Karadeniz Technical University, Department of Economics, 61080, Trabzon/Türkiye
28816