Araştırma Makalesi
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Teknolojik Dönüşümün Ekonomik Yansımaları: Türkiye'de Ar-Ge Harcamalarının Sektörel Yapısı ve Verimlilikle İlişkisi

Yıl 2025, Cilt: 3 Sayı: 2, 157 - 176, 30.12.2025

Öz

Amaç – Bu çalışma, 1990-2019 yılları arasında Türkiye'de Ar-Ge harcamalarının sektörel yapısının Toplam Faktör Verimliliği (TFV) üzerindeki etkilerini incelemektedir. Araştırma, özel sektör, kamu sektörü ve yükseköğretim kurumlarının Ar-Ge harcamalarının TFV'yi nasıl farklı şekilde etkilediğini inceleyerek, sektörel Ar-Ge etkilerine dair ampirik kanıtların sınırlı kaldığı gelişmekte olan ekonomiler literatüründeki kritik bir boşluğu ele almaktadır.
Tasarım/veri/metodoloji – Çalışmada, 1990-2019 dönemini kapsayan zaman serisi verileri kullanılmaktadır. Birim kök testleri (ADF, Phillips-Perron, KPSS), Johansen Eşbütünleşme Analizi, Vektör Hata Düzeltme Modeli (VECM) ve Temel Bileşen Analizi (PCA) gibi gelişmiş teknikler uygulanmaktadır. Veri seti, Türkiye İstatistik Kurumu, Penn World Table, Dünya Bankası ve OECD veri tabanlarından TFV ölçümlerini ve çeşitli makroekonomik, kurumsal ve Ar-Ge harcama değişkenlerini içermektedir.
Bulgular – Özel sektör Ar-Ge harcamaları ile TFV arasında negatif ve istatistiksel olarak anlamlı ilişki (-0,0073) ortaya koymaktadır. Kamu sektörü Ar-Ge'si (0,0112) ve yükseköğretim Ar-Ge'si (0,0108), üretkenlik üzerinde olumlu etkiler göstermektedir. Sermaye yoğunluğu faktörü, TFV üzerinde en güçlü olumlu etkiyi göstermektedir (0,0938), bu da Türkiye'deki üretkenlik kazanımlarının inovasyon odaklı büyümeden ziyade öncelikli olarak sermaye birikiminden kaynaklandığını göstermektedir.
Özgünlük/değer – Bu çalışma, Türkiye'de sektörel Ar-Ge harcamalarının üretkenlik üzerindeki farklı etkilerine dair ampirik kanıtlar sunarak literatüre katkıda bulunmakta ve özel sektör Ar-Ge etkinliğine ilişkin geleneksel varsayımları sorgulamaktadır.

Kaynakça

  • Acemoglu, D., & Robinson, J. A. (2013). Why Nations Fail: The Origins of Power, Prosperity, and Poverty. Crown Currency.
  • Aghion, P., & Howitt, P. (1990). A Model of Growth Through Creative Destruction. 2(60), 323–351. https://doi.org/10.3386/w3223
  • Aghion, P., & Saint-Paul, G. (1998). Virtues of bad times: Interaction between productivity growth and economic fluctuations. Macroeconomic Dynamics, 2(3), 322–344. https://doi.org/10.1017/S1365100598008025
  • Akcigit, U., Hanley, D., & Serrano-Velarde, N. (2021). Back to Basics: Basic Research Spillovers, Innovation Policy, and Growth. The Review of Economic Studies, 88(1), 1–43. https://doi.org/10.1093/restud/rdaa061
  • Akcigit, U., & Kerr, W. R. (2018). Growth through heterogeneous innovations. Journal of Political Economy, 4(126), 1374–1443.
  • Andrews, D., Criscuolo, C., & Gal, P. N. (2016). The best versus the rest: the global productivity slowdown, divergence across firms and the role of public policy (5; Productivity Working Papers).
  • Bloom, N., Jones, C. I., Van Reenen, J., & Webb, M. (2020). Are Ideas Getting Harder to Find? American Economic Review, 110(4), 1104–1144. https://doi.org/10.1257/aer.20180338
  • Breusch, T. S., & Pagan, A. R. (1979). A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica, 47(5), 1287. https://doi.org/10.2307/1911963
  • Caselli, F. (2005). Accounting for Cross-Country Income Differences. In Handbook of Economic Growth (Vol. 1, pp. 679–741). https://doi.org/10.1016/S1574-0684(05)01009-9
  • Castellani, D., Piva, M., Schubert, T., & Vivarelli, M. (2019). R&D and productivity in the US and the EU: Sectoral specificities and differences in the crisis. Technological Forecasting and Social Change, 138, 279–291. https://doi.org/10.1016/j.techfore.2018.10.001
  • Çetinsaya, G. (2020). Büyüme, kalite, uluslararasilaşma: Türkiye yükseköğretimi için bir yol haritasi. Cirera, X., & Maloney, W. F. (2017). The innovation paradox: Developing-country capabilities and the unrealized promise of technological catch-up. World Bank Publications.
  • Coe, D. T., & Helpman, E. (1995). International R&D spillovers. European Economic Review, 39(5), 859–887. https://doi.org/10.1016/0014-2921(94)00100-E
  • Coe, D. T., Helpman, E., & Hoffmaister, A. W. (2009). International R&D spillovers and institutions. European Economic Review, 53(7), 723–741. https://doi.org/10.1016/j.euroecorev.2009.02.005
  • Comin, D., & Mestieri, M. (2018). If Technology Has Arrived Everywhere, Why Has Income Diverged? American Economic Journal: Macroeconomics, 10(3), 137–178. https://doi.org/10.1257/mac.20150175
  • Crespi, G., & Zuniga, P. (2012). Innovation and Productivity: Evidence from Six Latin American Countries. World Development, 40(2), 273–290. https://doi.org/10.1016/j.worlddev.2011.07.010
  • David, P. A., Hall, B. H., & Toole, A. A. (2000). Is public R&D a complement or substitute for private R&D? A review of the econometric evidence. Research Policy, 29(4–5), 497–529. https://doi.org/10.1016/S0048-7333(99)00087-6
  • Dickey, D. A., & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49(4), 1057. https://doi.org/10.2307/1912517
  • Doraszelski, U., & Jaumandreu, J. (2013). R&D and Productivity: Estimating Endogenous Productivity. The Review of Economic Studies, 80(4), 1338–1383. https://doi.org/10.1093/restud/rdt011
  • Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251. https://doi.org/10.2307/1913236
  • Erdil, E., Pamukçu, M. T., Akçomak, I. S., & Tiryakioglu, M. (2016). Bilgi, bilim, teknoloji ve yenilik: Kavramsal tartışma. In Bilim, Teknoloji ve Yenilik: Kavramlar, Kuramlar ve Politika (pp. 35–66). Bilgi Üniversitesi Yayınları.
  • Findik, D., & Beyhan, B. (2017). A Perceptual Measure of Innovation Performance: Firm-Level Evidence from Turkey. International Journal of Innovation and Technology Management, 14(06), 1750038. https://doi.org/10.1142/S0219877017500389
  • Greenwood, J., Hercowitz, Z., & Krusell, P. (1997). Long-run implications of investment-specific technological change. The American Economic Review, 342–362.
  • Grossman, G. M., & Helpman, E. (1993). Innovation and growth in the global economy. MIT press.
  • Guellec, D., & Van Pottelsberghe de la Potterie, B. (2004). From R&D to Productivity Growth: Do the Institutional Settings and the Source of Funds of R&D Matter? Oxford Bulletin of Economics and Statistics, 66(3), 353–378. https://doi.org/10.1111/j.1468-0084.2004.00083.x
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2013). Multivariate data analysis: Pearson new international edition. Pearson Higher Ed.
  • Hall, R. E., & Jones, C. I. (1999). Why do Some Countries Produce So Much More Output Per Worker than Others? The Quarterly Journal of Economics, 114(1), 83–116. https://doi.org/10.1162/003355399555954
  • Jolliffe, I. (2005). Principal Component Analysis. In Encyclopedia of Statistics in Behavioral Science. Wiley. https://doi.org/10.1002/0470013192.bsa501
  • Kancs, d’Artis, & Siliverstovs, B. (2016). R&D and non-linear productivity growth. Research Policy, 45(3), 634–646. https://doi.org/10.1016/j.respol.2015.12.001
  • Kantor, S., & Whalley, A. (2019). Research proximity and productivity: Long-term evidence from agriculture. Journal of Political Economy, 2(127), 819–854.
  • Keller, W. (2004). International Technology Diffusion. Journal of Economic Literature, 42(3), 752–782. https://doi.org/10.1257/0022051042177685
  • Kılıçaslan, Y., & Taymaz, E. (2009). Labor market institutions and industrial performance: an evolutionary study. In Schumpeterian Perspectives on Innovation, Competition and Growth (pp. 207–222). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-93777-7_12
  • OECD. (2023). Main Science and Technology Indicators OECD Science, Technology and R&D Statistics (database).
  • Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis. In conometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (pp. 371–413). Cambridge University Press.
  • Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
  • Ramey, G., & Ramey, V. (1994). Cross-Country Evidence on the Link Between Volatility and Growth. American Economic Review, 5(85), 1138–1151. https://doi.org/10.3386/w4959
  • Ramsey, J. B. (1969). Tests for Specification Errors in Classical Linear Least-Squares Regression Analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology, 31(2), 350–371. https://doi.org/10.1111/j.2517-6161.1969.tb00796.x
  • Romer, P. M. (1990). Endogenous Technological Change. Journal of Political Economy, 98(5, Part 2), S71–S102. https://doi.org/10.1086/261725
  • Saygılı, Ş. (2003). Bilgi ekonomisine geçiş sürecinde Türkiye ekonomisinin dünyadaki konumu.
  • Solow, R. M. (1956). A Contribution to the Theory of Economic Growth. The Quarterly Journal of Economics, 70(1), 65. https://doi.org/10.2307/1884513
  • TÜİK. (2022). Araştırma-Geliştirme Faaliyetleri Araştırması, 2021.
  • Ugur, M., Trushin, E., Solomon, E., & Guidi, F. (2016). R&D and productivity in OECD firms and industries: A hierarchical meta-regression analysis. Research Policy, 45(10), 2069–2086. https://doi.org/10.1016/j.respol.2016.08.001
  • Valero, A., & Van Reenen, J. (2019). The economic impact of universities: Evidence from across the globe. Economics of Education Review, 68, 53–67. https://doi.org/10.1016/j.econedurev.2018.09.001
  • White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817. https://doi.org/10.2307/1912934
  • Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach (5th ed.). South-Western Cengage Learning.

Economic Reflections of Technological Transformation: Sectoral Structure of R&D Expenditures in Türkiye and Their Relationship with Productivity

Yıl 2025, Cilt: 3 Sayı: 2, 157 - 176, 30.12.2025

Öz

Purpose – This study examines the effects of sectoral composition of Research and Development (R&D) expenditures on Total Factor Productivity (TFP) in Türkiye during 1990-2019. The research investigates how R&D expenditures by private sector, public sector, and higher education institutions differentially impact TFP, addressing a critical gap in the literature on developing economies where empirical evidence on sectoral R&D impacts remains limited.
Design/data/methodology – The study employs time-series data covering 1990-2019. Sophisticated techniques including unit root tests (ADF, Phillips-Perron, KPSS), Johansen Cointegration Analysis, Vector Error Correction Model (VECM), and Principal Component Analysis (PCA) are applied. The dataset includes TFP measurements and various macroeconomic, institutional, and R&D expenditure variables from Turkish Statistical Institute, Penn World Table, World Bank, and OECD databases.
Findings – Contrary to conventional expectations, the findings reveal an unexpected negative and statistically significant relationship (-0.0073) between private sector R&D expenditures and TFP. Public sector R&D (0.0112) and higher education R&D (0.0108) demonstrate positive effects on productivity. The capital intensity factor shows the strongest positive effect on TFP (0.0938), suggesting productivity gains in Türkiye primarily stem from capital accumulation rather than innovation-driven growth.
Originality/value – This study contributes to the literature by providing empirical evidence on the differential impacts of sectoral R&D expenditures on productivity in Türkiye, challenging conventional assumptions about private sector R&D effectiveness.

Kaynakça

  • Acemoglu, D., & Robinson, J. A. (2013). Why Nations Fail: The Origins of Power, Prosperity, and Poverty. Crown Currency.
  • Aghion, P., & Howitt, P. (1990). A Model of Growth Through Creative Destruction. 2(60), 323–351. https://doi.org/10.3386/w3223
  • Aghion, P., & Saint-Paul, G. (1998). Virtues of bad times: Interaction between productivity growth and economic fluctuations. Macroeconomic Dynamics, 2(3), 322–344. https://doi.org/10.1017/S1365100598008025
  • Akcigit, U., Hanley, D., & Serrano-Velarde, N. (2021). Back to Basics: Basic Research Spillovers, Innovation Policy, and Growth. The Review of Economic Studies, 88(1), 1–43. https://doi.org/10.1093/restud/rdaa061
  • Akcigit, U., & Kerr, W. R. (2018). Growth through heterogeneous innovations. Journal of Political Economy, 4(126), 1374–1443.
  • Andrews, D., Criscuolo, C., & Gal, P. N. (2016). The best versus the rest: the global productivity slowdown, divergence across firms and the role of public policy (5; Productivity Working Papers).
  • Bloom, N., Jones, C. I., Van Reenen, J., & Webb, M. (2020). Are Ideas Getting Harder to Find? American Economic Review, 110(4), 1104–1144. https://doi.org/10.1257/aer.20180338
  • Breusch, T. S., & Pagan, A. R. (1979). A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica, 47(5), 1287. https://doi.org/10.2307/1911963
  • Caselli, F. (2005). Accounting for Cross-Country Income Differences. In Handbook of Economic Growth (Vol. 1, pp. 679–741). https://doi.org/10.1016/S1574-0684(05)01009-9
  • Castellani, D., Piva, M., Schubert, T., & Vivarelli, M. (2019). R&D and productivity in the US and the EU: Sectoral specificities and differences in the crisis. Technological Forecasting and Social Change, 138, 279–291. https://doi.org/10.1016/j.techfore.2018.10.001
  • Çetinsaya, G. (2020). Büyüme, kalite, uluslararasilaşma: Türkiye yükseköğretimi için bir yol haritasi. Cirera, X., & Maloney, W. F. (2017). The innovation paradox: Developing-country capabilities and the unrealized promise of technological catch-up. World Bank Publications.
  • Coe, D. T., & Helpman, E. (1995). International R&D spillovers. European Economic Review, 39(5), 859–887. https://doi.org/10.1016/0014-2921(94)00100-E
  • Coe, D. T., Helpman, E., & Hoffmaister, A. W. (2009). International R&D spillovers and institutions. European Economic Review, 53(7), 723–741. https://doi.org/10.1016/j.euroecorev.2009.02.005
  • Comin, D., & Mestieri, M. (2018). If Technology Has Arrived Everywhere, Why Has Income Diverged? American Economic Journal: Macroeconomics, 10(3), 137–178. https://doi.org/10.1257/mac.20150175
  • Crespi, G., & Zuniga, P. (2012). Innovation and Productivity: Evidence from Six Latin American Countries. World Development, 40(2), 273–290. https://doi.org/10.1016/j.worlddev.2011.07.010
  • David, P. A., Hall, B. H., & Toole, A. A. (2000). Is public R&D a complement or substitute for private R&D? A review of the econometric evidence. Research Policy, 29(4–5), 497–529. https://doi.org/10.1016/S0048-7333(99)00087-6
  • Dickey, D. A., & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49(4), 1057. https://doi.org/10.2307/1912517
  • Doraszelski, U., & Jaumandreu, J. (2013). R&D and Productivity: Estimating Endogenous Productivity. The Review of Economic Studies, 80(4), 1338–1383. https://doi.org/10.1093/restud/rdt011
  • Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251. https://doi.org/10.2307/1913236
  • Erdil, E., Pamukçu, M. T., Akçomak, I. S., & Tiryakioglu, M. (2016). Bilgi, bilim, teknoloji ve yenilik: Kavramsal tartışma. In Bilim, Teknoloji ve Yenilik: Kavramlar, Kuramlar ve Politika (pp. 35–66). Bilgi Üniversitesi Yayınları.
  • Findik, D., & Beyhan, B. (2017). A Perceptual Measure of Innovation Performance: Firm-Level Evidence from Turkey. International Journal of Innovation and Technology Management, 14(06), 1750038. https://doi.org/10.1142/S0219877017500389
  • Greenwood, J., Hercowitz, Z., & Krusell, P. (1997). Long-run implications of investment-specific technological change. The American Economic Review, 342–362.
  • Grossman, G. M., & Helpman, E. (1993). Innovation and growth in the global economy. MIT press.
  • Guellec, D., & Van Pottelsberghe de la Potterie, B. (2004). From R&D to Productivity Growth: Do the Institutional Settings and the Source of Funds of R&D Matter? Oxford Bulletin of Economics and Statistics, 66(3), 353–378. https://doi.org/10.1111/j.1468-0084.2004.00083.x
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2013). Multivariate data analysis: Pearson new international edition. Pearson Higher Ed.
  • Hall, R. E., & Jones, C. I. (1999). Why do Some Countries Produce So Much More Output Per Worker than Others? The Quarterly Journal of Economics, 114(1), 83–116. https://doi.org/10.1162/003355399555954
  • Jolliffe, I. (2005). Principal Component Analysis. In Encyclopedia of Statistics in Behavioral Science. Wiley. https://doi.org/10.1002/0470013192.bsa501
  • Kancs, d’Artis, & Siliverstovs, B. (2016). R&D and non-linear productivity growth. Research Policy, 45(3), 634–646. https://doi.org/10.1016/j.respol.2015.12.001
  • Kantor, S., & Whalley, A. (2019). Research proximity and productivity: Long-term evidence from agriculture. Journal of Political Economy, 2(127), 819–854.
  • Keller, W. (2004). International Technology Diffusion. Journal of Economic Literature, 42(3), 752–782. https://doi.org/10.1257/0022051042177685
  • Kılıçaslan, Y., & Taymaz, E. (2009). Labor market institutions and industrial performance: an evolutionary study. In Schumpeterian Perspectives on Innovation, Competition and Growth (pp. 207–222). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-93777-7_12
  • OECD. (2023). Main Science and Technology Indicators OECD Science, Technology and R&D Statistics (database).
  • Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis. In conometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (pp. 371–413). Cambridge University Press.
  • Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
  • Ramey, G., & Ramey, V. (1994). Cross-Country Evidence on the Link Between Volatility and Growth. American Economic Review, 5(85), 1138–1151. https://doi.org/10.3386/w4959
  • Ramsey, J. B. (1969). Tests for Specification Errors in Classical Linear Least-Squares Regression Analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology, 31(2), 350–371. https://doi.org/10.1111/j.2517-6161.1969.tb00796.x
  • Romer, P. M. (1990). Endogenous Technological Change. Journal of Political Economy, 98(5, Part 2), S71–S102. https://doi.org/10.1086/261725
  • Saygılı, Ş. (2003). Bilgi ekonomisine geçiş sürecinde Türkiye ekonomisinin dünyadaki konumu.
  • Solow, R. M. (1956). A Contribution to the Theory of Economic Growth. The Quarterly Journal of Economics, 70(1), 65. https://doi.org/10.2307/1884513
  • TÜİK. (2022). Araştırma-Geliştirme Faaliyetleri Araştırması, 2021.
  • Ugur, M., Trushin, E., Solomon, E., & Guidi, F. (2016). R&D and productivity in OECD firms and industries: A hierarchical meta-regression analysis. Research Policy, 45(10), 2069–2086. https://doi.org/10.1016/j.respol.2016.08.001
  • Valero, A., & Van Reenen, J. (2019). The economic impact of universities: Evidence from across the globe. Economics of Education Review, 68, 53–67. https://doi.org/10.1016/j.econedurev.2018.09.001
  • White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817. https://doi.org/10.2307/1912934
  • Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach (5th ed.). South-Western Cengage Learning.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makro İktisat (Diğer), Uygulamalı Ekonomi (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Mücahit Çitil 0000-0002-6788-7115

Gönderilme Tarihi 7 Ağustos 2025
Kabul Tarihi 5 Ekim 2025
Yayımlanma Tarihi 30 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 3 Sayı: 2

Kaynak Göster

APA Çitil, M. (2025). Economic Reflections of Technological Transformation: Sectoral Structure of R&D Expenditures in Türkiye and Their Relationship with Productivity. Journal of Economics, Finance and Sustainability, 3(2), 157-176.
AMA Çitil M. Economic Reflections of Technological Transformation: Sectoral Structure of R&D Expenditures in Türkiye and Their Relationship with Productivity. EFS. Aralık 2025;3(2):157-176.
Chicago Çitil, Mücahit. “Economic Reflections of Technological Transformation: Sectoral Structure of R&D Expenditures in Türkiye and Their Relationship with Productivity”. Journal of Economics, Finance and Sustainability 3, sy. 2 (Aralık 2025): 157-76.
EndNote Çitil M (01 Aralık 2025) Economic Reflections of Technological Transformation: Sectoral Structure of R&D Expenditures in Türkiye and Their Relationship with Productivity. Journal of Economics, Finance and Sustainability 3 2 157–176.
IEEE M. Çitil, “Economic Reflections of Technological Transformation: Sectoral Structure of R&D Expenditures in Türkiye and Their Relationship with Productivity”, EFS, c. 3, sy. 2, ss. 157–176, 2025.
ISNAD Çitil, Mücahit. “Economic Reflections of Technological Transformation: Sectoral Structure of R&D Expenditures in Türkiye and Their Relationship with Productivity”. Journal of Economics, Finance and Sustainability 3/2 (Aralık2025), 157-176.
JAMA Çitil M. Economic Reflections of Technological Transformation: Sectoral Structure of R&D Expenditures in Türkiye and Their Relationship with Productivity. EFS. 2025;3:157–176.
MLA Çitil, Mücahit. “Economic Reflections of Technological Transformation: Sectoral Structure of R&D Expenditures in Türkiye and Their Relationship with Productivity”. Journal of Economics, Finance and Sustainability, c. 3, sy. 2, 2025, ss. 157-76.
Vancouver Çitil M. Economic Reflections of Technological Transformation: Sectoral Structure of R&D Expenditures in Türkiye and Their Relationship with Productivity. EFS. 2025;3(2):157-76.

Journal of Economics, Finance and Sustainability
Recep Tayyip Erdoğan Üniversitesi Mezunlar Derneği
RİZE / TÜRKİYE