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COMPARISON OF AGRICULTURE AND SERVICE SECTORS BASED ON LEARNING CURVES: THE CASE OF EU-27, UNITED KINGDOM AND TURKEY

Year 2024, Volume: 19 Issue: 61, 50 - 76, 26.01.2024

Zeynep Sungur Haydar Akyazı

https://doi.org/10.14783/maruoneri.1411292

Abstract

The theory of learning curve is based on the idea that the unit production cost will decrease after the increase in efficiency increased by specializing as a result of the recurrence of the same tasks in the production process. The cost decrease in the production process gives companies a competitive advantage. Therefore, it is important to determine the factors that accelerate the learning process. The aim of this study is to compare the learning potential of the countries by obtaining learning rates in the agricultural and service sectors for the 2004-2020 period in the EU-27, UK and Turkey sample. Within the scope of this purpose, the research model is first examined by panel data analysis and the learning flexibility parameter is estimated. In the next stage, learning rates for the agricultural and service sectors of each country are calculated by using the learning flexibility parameter. The results of the study show that learning in agricultural and service sectors in Turkey is lower than average learning level of EU countries. The study offers policy proposals that will accelerate learning in agricultural and service sectors in Turkey.

Keywords

Learning Learning Flexibility Learning Rate Learning Curves

References

  • Albeni, M. (2004). Türkiye’de Teknolojik Öğrenmenin Alansal Analizi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22, 19-37.
  • Asgari, B. & Gonzalez-Cortez, J. L. (2012). Measurement of Technological Progress Through Analysis of Learning Rates; The Case of Manufacturing Industry in Mexico. Ritsumeikan Journal of Asia Pacific Studies, 31, 101-119.
  • Asgari, B. & Yen, L. W. (2009). Accumulated Knowledge and Technological Progress in terms of Learning Rates: A Comparative Analysis on the Manufacturing Industry and the Service Industry in Malaysia. Asian Journal of Technology Innovation, 17(2), 71-99.
  • Aydın, M.K. & Aksoy, Ö. (2014). Öğrenme’nin ‘Kısa Dönem’ Koşulları Altında Maliyet Azaltıcı Etkisi Üzerine Bir Değerlendirme. Bilgi, 29, 1-15.
  • Badiru, A.B. (1992). Computational Survey of Univariate and Multivariate Learning Curve Models. IEE Transaction on Engineering Management, 39(2), 176-188.
  • Bagodi, V. & Mahanty, B. (2013). Double Loop Learning in the Indian Two-Wheeler Service Sector. The Learning Organization, 20(415), 264-278.
  • Baltagi, B. (2003). A companion to theoritical econometrics. Blackwel Publishing, Germany
  • Begg, C.B., Cramer, L.D., Hoskins, W.J. & Brennan, M.F. (1998). Impact of Hospital Volume on Operative Mortality for Major Cancer Surgery. Hospital Volume and Operative Mortality, 280(20), 1747-1751.
  • Boone, T., Ganeshan, R. & Hicks, R.L. (2008). Learning and Knowledge Depreciation in Professional Services. Management Science, 53(8), 1315-1331.
  • Cohen, D. (1987). Educational technology, policy, and practice. Educational Evaluation and Policy Analysis, 9, 153-170.
  • Garibaldi, L.A., Aizen, M.A., Klein, A.M., Cunningham, S.A. & Harder, L.H. (2011). Global Growth and Stability of Agricultural Yield Decrease with Pollinator Dependence. Proceeding of the National Academy of Sciences of the United States of America, 108(14), 5909-5914.
  • Glock, C.H., Grosse, E.H., Jaber, M.Y. & Smunt, T.L. (2019). Applications of Learning Curves in Production and Operations Management: A Systematic Literature Review. Computers & Industrial Engineering, 131, 422-441.
  • Hayami, Y. (1969). Resource Endowments and Technological Change in Agriculture: U.S. and Japanese Experiences in İnternational Perspective. American Journal of Agricultural Economics, 51(5), 1293-1303.
  • Heng, T.M. (2010). Learning Curves & Productivity in Singapore Manufacturing Industries. Second Annual Conference of the Academic Network for Development in Asia (ANDA), Phnom Penh, Cambodia.
  • Heng, T.M. & Low, L. (1995). Estimating and Comparing Learning Curves in Three Asian Economies. Asia Pacific Journal of Management, 12, 21-35.
  • Hillner, B.E. & Smith, T.J. (1998). Hospital Voluma and Patient Outcomes in Major Cancer Surgery. JAMA, 280(20), 1747-1748.
  • Hinkin, T.R. & Tracey, J.B. (2000). The Cost of Turnover. Cornell Hotel and Restaurant Administration Quarterly, 41(14), 14-21.
  • Jaber, M.Y. (Ed.) (2011). Learning Curves: Theory, Models and Applications. CRC Press (Taylor & Francis Group), Boca Raton, FL.
  • Johnson, C. (1982). MITI and Japanese miracle: The growth of industrial policy, 1925–1975. Standford University Press, Stanford.
  • Jollis, J.G., Peterson, E.D., DeLong, E.R., Mark, D.B., Collins, S.R., Muhlbaier, L.H. & Pryor, D.B. (1994). The Relation Between the Volume of Coronary Angioplasty Procedures at Hospitals Treating Medicare Beneficiaries and Short-term Mortality. New England Journal of Medicine, 331(24), 1625-1629.
  • Kang, T. W. (1987). Is Korea the next Japan? Understanding the structure, strategy and tactics of America’s next competitor. Free Press, New York.
  • Lapre, M.A. & Nembhard, I.M. (2010). Inside the Organizational Learning Curve: Understanding the Organizational Learning Process. Foundations and Trends in Technology, Information and Operations Management, 4(1), 1-103.
  • Lincoln, E. J. (1988). Japan: Facing economic maturity. Brookings Institutions, Washington, DC.
  • McCoskey, S. & Kao, C. (1998). A Residual-Based of the Null Hypothesis of Cointegration in Panel Data. Econometrics Reviews, 17, 57-84.
  • Marra, M., Pannell, D.J. & Ghadim, A.A. (2003). The Economics of Risk, Uncertainity and Learning in the Adoption of New Agricultural Technologies: Where Are We on the Learning Curve?. Agricultural Systems, 75(2-3), 215-234.
  • McGill, F.B. (2000). The Meaning of Service: Ambiguties and Dilemmas for Public Library Service Providers. Library & Information Science Research, 22(3), 243-272.
  • Morishima, M. (1982). Why has Japan ‘succeeded’? Western technology and the Japanese ethos. Cambridge University Press.
  • Pesaran, M. H. (2004). General Diagnostic Test for Cross Section Dependence in Panels. University of Cambridge, Working Paper, 0435.
  • Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panel with a Multifactor Error Structure. Econometrica, 74(4), 967-1012.
  • Pesaran, M. H. (2007). A Simple Panel Unit Root Test in the Presence of Cross Section Dependence. Journal of Applied Econometrics, 22(2), 265-312.
  • Promongkit, P., Shawyun, T. & Sirinaovakul, B. (2002). Productivity Growth and Learning Potentials of Thai Industry. Technological Forecasting and Social Change, 69(1), 89-101.
  • Promongkit, P., Shawyun, T. & Sirinaovakul, B. (2000). Analysis of Technological Learning for the Thai Manufacturing Industry. Technovation, 20(4), 189-195.
  • Stevenson, W.J. (1996). Production / Operation Management. Irvin Publishing, 5th Edition.
  • Sungur, Z. (2022). Öğrenme Eğrileri: Teori ve Sektörel Öğrenme Eğrilerinin Tahmin Edilmesi Üzerine Ampirik Bir Çalışma. (Yayınlanmamış doktora tezi). Karadeniz Teknik Üniversitesi, Trabzon.
  • Swamy, P. (1970). Efficient Inference in a Random Coefficient Regression Model. Econometrica, 38(2), 311-323.
  • Swamy, P. (1971). Statistical Inference in Random Coefficient Regression Models. Springer-Verlag, Berlin.
  • Tatoğlu, F. Y. (2017). Panel Zaman Serileri Analizi Stata Uygulamalı. Beta Yayınevi, İstanbul.
  • Thimann, D.R., Coresh, J., Oetgen, W. & Powe, N. (1999). The Association Between Hospital Volume and Survival After Acute Myocardial Infarction in Elderly Patients. The New England Journal of Medicine, 340(21), 1640-1648.
  • Zamanian, G.H.R., Shahabinejad, V. & Yaghoubi, M. (2013). Application of DEA and SFA on the Measurement of Agricultural Technical Efficiency in MENA Countries. International Journal of Applied Operational Research, 3(2), 43-51.
  • Vogel, C. R. (1987). Computational methods for inverse problems. Society for Industrial and Applied Mathematics, Philadelphia.
  • Wennberg, D. E., Lucas, F.L., Birkmeyer, J. D., Bredenberg C. E. & Fisher, E. S. (1998). Variation in Carotid Endarterectomy Mortality in the Medicare Population. JAMA, 279(16), 1278-1281.
  • Westerlund, J. (2008). Panel Cointegration Tests of the Fisher Effect. Journal of Applied Econometrics, 23(2), 193-233.
  • Westerlund, J. & Edgerton, D. (2007). A Panel Bootstrap Cointegration Test. Economics Letters, 97, 185-190.
  • Wright, T.P. (1936). Factors Affecting the Cost of Airplanes. Journal of Aeronautical Sciences, 3(2), 122-128.

ÖĞRENME EĞRİLERİ TEMELİNDE TARIM VE HİZMET SEKTÖRLERİNİN KARŞILAŞTIRILMASI: AB-27, BİRLEŞİK KRALLIK ve TÜRKİYE ÖRNEĞİ

Year 2024, Volume: 19 Issue: 61, 50 - 76, 26.01.2024

Zeynep Sungur Haydar Akyazı

https://doi.org/10.14783/maruoneri.1411292

Abstract

Öğrenme eğrisi teorisi, bireylerin üretim sürecinde aynı görevleri tekrarlamaları sonucunda uzmanlaşarak elde ettikleri verimlilik artışı sonrasında birim üretim maliyetinin düşeceği düşüncesine dayanmaktadır. Üretim sürecindeki maliyet düşüşü firmalara rekabet avantajı kazandırmaktadır. Bu nedenle de öğrenme sürecini hızlandıran faktörlerin belirlenmesi önemlidir. Bu çalışmada amaç, AB-27, Birleşik Krallık ve Türkiye örnekleminde 2004-2020 dönemi için tarım ve hizmet sektörlerinde öğrenme oranlarının elde edilerek ülkelerin öğrenme potansiyellerini karşılaştırmaktır. Bu amaç kapsamında ilk önce araştırma modeli panel veri analizi ile incelenerek öğrenme esneklik parametresi tahmin edilmektedir. Sonraki aşamada öğrenme esneklik parametresi kullanılarak her ülkenin tarım ve hizmet sektörleri için öğrenme oranları hesaplanmaktadır. Çalışmanın sonuçları, Türkiye’de tarım ve hizmet sektörlerinde öğrenmenin AB ülkeleri ortalamasından düşük olduğunu göstermektedir. Çalışma, Türkiye’de tarım ve hizmet sektörlerinde öğrenmeyi hızlandıracak politika önerileri sunmaktadır.

Keywords

Öğrenme Öğrenme Esnekliği Öğrenme Oranı Öğrenme Eğrileri

References

  • Albeni, M. (2004). Türkiye’de Teknolojik Öğrenmenin Alansal Analizi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22, 19-37.
  • Asgari, B. & Gonzalez-Cortez, J. L. (2012). Measurement of Technological Progress Through Analysis of Learning Rates; The Case of Manufacturing Industry in Mexico. Ritsumeikan Journal of Asia Pacific Studies, 31, 101-119.
  • Asgari, B. & Yen, L. W. (2009). Accumulated Knowledge and Technological Progress in terms of Learning Rates: A Comparative Analysis on the Manufacturing Industry and the Service Industry in Malaysia. Asian Journal of Technology Innovation, 17(2), 71-99.
  • Aydın, M.K. & Aksoy, Ö. (2014). Öğrenme’nin ‘Kısa Dönem’ Koşulları Altında Maliyet Azaltıcı Etkisi Üzerine Bir Değerlendirme. Bilgi, 29, 1-15.
  • Badiru, A.B. (1992). Computational Survey of Univariate and Multivariate Learning Curve Models. IEE Transaction on Engineering Management, 39(2), 176-188.
  • Bagodi, V. & Mahanty, B. (2013). Double Loop Learning in the Indian Two-Wheeler Service Sector. The Learning Organization, 20(415), 264-278.
  • Baltagi, B. (2003). A companion to theoritical econometrics. Blackwel Publishing, Germany
  • Begg, C.B., Cramer, L.D., Hoskins, W.J. & Brennan, M.F. (1998). Impact of Hospital Volume on Operative Mortality for Major Cancer Surgery. Hospital Volume and Operative Mortality, 280(20), 1747-1751.
  • Boone, T., Ganeshan, R. & Hicks, R.L. (2008). Learning and Knowledge Depreciation in Professional Services. Management Science, 53(8), 1315-1331.
  • Cohen, D. (1987). Educational technology, policy, and practice. Educational Evaluation and Policy Analysis, 9, 153-170.
  • Garibaldi, L.A., Aizen, M.A., Klein, A.M., Cunningham, S.A. & Harder, L.H. (2011). Global Growth and Stability of Agricultural Yield Decrease with Pollinator Dependence. Proceeding of the National Academy of Sciences of the United States of America, 108(14), 5909-5914.
  • Glock, C.H., Grosse, E.H., Jaber, M.Y. & Smunt, T.L. (2019). Applications of Learning Curves in Production and Operations Management: A Systematic Literature Review. Computers & Industrial Engineering, 131, 422-441.
  • Hayami, Y. (1969). Resource Endowments and Technological Change in Agriculture: U.S. and Japanese Experiences in İnternational Perspective. American Journal of Agricultural Economics, 51(5), 1293-1303.
  • Heng, T.M. (2010). Learning Curves & Productivity in Singapore Manufacturing Industries. Second Annual Conference of the Academic Network for Development in Asia (ANDA), Phnom Penh, Cambodia.
  • Heng, T.M. & Low, L. (1995). Estimating and Comparing Learning Curves in Three Asian Economies. Asia Pacific Journal of Management, 12, 21-35.
  • Hillner, B.E. & Smith, T.J. (1998). Hospital Voluma and Patient Outcomes in Major Cancer Surgery. JAMA, 280(20), 1747-1748.
  • Hinkin, T.R. & Tracey, J.B. (2000). The Cost of Turnover. Cornell Hotel and Restaurant Administration Quarterly, 41(14), 14-21.
  • Jaber, M.Y. (Ed.) (2011). Learning Curves: Theory, Models and Applications. CRC Press (Taylor & Francis Group), Boca Raton, FL.
  • Johnson, C. (1982). MITI and Japanese miracle: The growth of industrial policy, 1925–1975. Standford University Press, Stanford.
  • Jollis, J.G., Peterson, E.D., DeLong, E.R., Mark, D.B., Collins, S.R., Muhlbaier, L.H. & Pryor, D.B. (1994). The Relation Between the Volume of Coronary Angioplasty Procedures at Hospitals Treating Medicare Beneficiaries and Short-term Mortality. New England Journal of Medicine, 331(24), 1625-1629.
  • Kang, T. W. (1987). Is Korea the next Japan? Understanding the structure, strategy and tactics of America’s next competitor. Free Press, New York.
  • Lapre, M.A. & Nembhard, I.M. (2010). Inside the Organizational Learning Curve: Understanding the Organizational Learning Process. Foundations and Trends in Technology, Information and Operations Management, 4(1), 1-103.
  • Lincoln, E. J. (1988). Japan: Facing economic maturity. Brookings Institutions, Washington, DC.
  • McCoskey, S. & Kao, C. (1998). A Residual-Based of the Null Hypothesis of Cointegration in Panel Data. Econometrics Reviews, 17, 57-84.
  • Marra, M., Pannell, D.J. & Ghadim, A.A. (2003). The Economics of Risk, Uncertainity and Learning in the Adoption of New Agricultural Technologies: Where Are We on the Learning Curve?. Agricultural Systems, 75(2-3), 215-234.
  • McGill, F.B. (2000). The Meaning of Service: Ambiguties and Dilemmas for Public Library Service Providers. Library & Information Science Research, 22(3), 243-272.
  • Morishima, M. (1982). Why has Japan ‘succeeded’? Western technology and the Japanese ethos. Cambridge University Press.
  • Pesaran, M. H. (2004). General Diagnostic Test for Cross Section Dependence in Panels. University of Cambridge, Working Paper, 0435.
  • Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panel with a Multifactor Error Structure. Econometrica, 74(4), 967-1012.
  • Pesaran, M. H. (2007). A Simple Panel Unit Root Test in the Presence of Cross Section Dependence. Journal of Applied Econometrics, 22(2), 265-312.
  • Promongkit, P., Shawyun, T. & Sirinaovakul, B. (2002). Productivity Growth and Learning Potentials of Thai Industry. Technological Forecasting and Social Change, 69(1), 89-101.
  • Promongkit, P., Shawyun, T. & Sirinaovakul, B. (2000). Analysis of Technological Learning for the Thai Manufacturing Industry. Technovation, 20(4), 189-195.
  • Stevenson, W.J. (1996). Production / Operation Management. Irvin Publishing, 5th Edition.
  • Sungur, Z. (2022). Öğrenme Eğrileri: Teori ve Sektörel Öğrenme Eğrilerinin Tahmin Edilmesi Üzerine Ampirik Bir Çalışma. (Yayınlanmamış doktora tezi). Karadeniz Teknik Üniversitesi, Trabzon.
  • Swamy, P. (1970). Efficient Inference in a Random Coefficient Regression Model. Econometrica, 38(2), 311-323.
  • Swamy, P. (1971). Statistical Inference in Random Coefficient Regression Models. Springer-Verlag, Berlin.
  • Tatoğlu, F. Y. (2017). Panel Zaman Serileri Analizi Stata Uygulamalı. Beta Yayınevi, İstanbul.
  • Thimann, D.R., Coresh, J., Oetgen, W. & Powe, N. (1999). The Association Between Hospital Volume and Survival After Acute Myocardial Infarction in Elderly Patients. The New England Journal of Medicine, 340(21), 1640-1648.
  • Zamanian, G.H.R., Shahabinejad, V. & Yaghoubi, M. (2013). Application of DEA and SFA on the Measurement of Agricultural Technical Efficiency in MENA Countries. International Journal of Applied Operational Research, 3(2), 43-51.
  • Vogel, C. R. (1987). Computational methods for inverse problems. Society for Industrial and Applied Mathematics, Philadelphia.
  • Wennberg, D. E., Lucas, F.L., Birkmeyer, J. D., Bredenberg C. E. & Fisher, E. S. (1998). Variation in Carotid Endarterectomy Mortality in the Medicare Population. JAMA, 279(16), 1278-1281.
  • Westerlund, J. (2008). Panel Cointegration Tests of the Fisher Effect. Journal of Applied Econometrics, 23(2), 193-233.
  • Westerlund, J. & Edgerton, D. (2007). A Panel Bootstrap Cointegration Test. Economics Letters, 97, 185-190.
  • Wright, T.P. (1936). Factors Affecting the Cost of Airplanes. Journal of Aeronautical Sciences, 3(2), 122-128.
There are 44 citations in total.

Details

Primary Language Turkish
Subjects Microeconomic Theory
Journal Section Makale Başvuru
Authors

Zeynep Sungur 0000-0001-5108-0416

Haydar Akyazı 0000-0002-9700-4512

Early Pub Date January 25, 2024
Publication Date January 26, 2024
Submission Date December 28, 2023
Acceptance Date January 10, 2024
Published in Issue Year 2024 Volume: 19 Issue: 61

Cite

APA Sungur, Z., & Akyazı, H. (2024). ÖĞRENME EĞRİLERİ TEMELİNDE TARIM VE HİZMET SEKTÖRLERİNİN KARŞILAŞTIRILMASI: AB-27, BİRLEŞİK KRALLIK ve TÜRKİYE ÖRNEĞİ. Öneri Dergisi, 19(61), 50-76. https://doi.org/10.14783/maruoneri.1411292

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Öneri

Marmara UniversityInstitute of Social Sciences

Göztepe Kampüsü Enstitüler Binası Kat:5 34722  Kadıköy/İstanbul

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