TY  - JOUR
T1  - TÜRKİYE’NİN BÖLGESEL DÜZEYDE AR-GE GİRDİLERİNİN GM(1,1) MODELİ İLE TAHMİN EDİLMESİ
TT  - PREDICTION OF TÜRKİYE’S R&D INPUTS AT REGIONAL LEVEL BY GM(1,1) MODEL
AU  - Belgin, Önder
PY  - 2025
DA  - May
Y2  - 2025
DO  - 10.61138/bolgeselkalkinmadergisi.1652668
JF  - Bölgesel Kalkınma Dergisi
PB  - Sanayi ve Teknoloji Bakanlığı
WT  - DergiPark
SN  - 2980-0544
SP  - 38
EP  - 48
VL  - 03
IS  - 01
LA  - tr
AB  - Araştırma ve geliştirme (Ar-Ge), özellikle rekabet güçlerini artırmak isteyen gelişmekte olan ülkeler için ekonomik büyümenin önemli bir itici gücüdür. Ar-Ge ekosistemin çeşitli değişkenler bakımından performansının değerlendirilmesi, politika yapıcıların en iyi uygulamaları belirlemelerine, stratejileri iyileştirmelerine ve çeşitli aşamalar ve seviyelerdeki dinamikleri daha iyi anlamalarına olanak tanır. Bu çalışmada Türkiye’nin bölgesel düzeyde Ar-Ge girdilerinin (Ar-Ge İnsan Kaynağı ve Ar-Ge Harcamalarının Gayri Safi Yurtiçi Hasıladaki Payı) gri sistem teorisinin bir yöntemi olan GM(1,1) modeli ile 2024-2026 dönemi için tahmin edilmiştir. Çalışmada 2010-2023 yılları arasındaki geçmiş veriler kullanılmış ve tahminin hata oranı Ortalama Mutlak Yüzdesel Hata (Mean Absolute Percentage Error-MAPE) değerine göre değerlendirilmiştir. Buna göre, GM(1,1) modeli Ar-Ge İnsan Kaynağı değişkenin tahmininde daha başarılı tahmin değerleri sağlamıştır. Gelecek yıllara ilişkin yapılan tahminlerde Ar-Ge İnsan Kaynağının 2024-2026 döneminde tüm bölgelerde artacağı, Ar-Ge Harcamalarının Gayri Safi Yurtiçi Hasıladaki Payının TRC bölgesi haricinde artış göstereceği görülmüştür. Bu çalışma Türkiye’de bölgesel düzeyde Ar-Ge girdilerinin düzeyinin tahmin edilmesine ilişkin ilk çalışma olma özelliğini taşımaktadır.
KW  - Ar-Ge ve yenilik
KW  - Gri sistem teorisi
KW  - Tahmin
KW  - Bölgesel çalışmalar
N2  - Research and development (R&D) is an important driver of economic growth, especially for developing countries seeking to increase their competitiveness. Assessing the performance of the R&D ecosystem in terms of various variables allows policy makers to identify best practices, improve strategies and better understand the dynamics at various stages and levels. In this study, Türkiye's regional R&D inputs (the share of R&D Human Resources and R&D Expenditures in Gross Domestic Product) are predicted for the period 2024-2026 with the GM(1,1) model, which is a method of grey system theory. In the study, historical data between 2010-2023 were used and the error rate of the prediction was evaluated according to the Mean Absolute Percentage Error (MAPE) value. Accordingly, the GM(1,1) model provided more successful prediction values in the prediction of the R&D Human Resources variable. In the predictions made for the future years, it is seen that R&D Human Resources will increase in all regions in the period 2024-2026, and the Share of R&D Expenditures in Gross Domestic Product will increase except for the TRC region. This study is the first study in Türkiye to estimate the level of R&D inputs at regional level.
CR  - Afzal, M. N. I. (2014). An empirical investigation of the National Innovation System (NIS) Using Data Envelopment Analysis (DEA) and the TOBIT Model. International Review of Applied Economics, 28 (4), 507-523. https://doi.org/10.1080 /02692171.2014.896880
CR  - Akyüz, L. ve Bilgili, H. (2022). GM (1,1) ve EXGM (1,1) Tahmin Modellerinin Türkiye’nin Ar-Ge Harcamalarına Uygulanması. Aksaray University Journal of Science and Engineering, 6 (2), 95-106. https://doi.org/10.29002/ asujse.1087288
CR  - Chen, C. P., Hu, J. L., and Yang, C. H. (2013). Produce Patents or Journal Articles? A Cross-Country Comparison of R&D Productivity Change. Scientometrics, 94 (3), 833–849. https://doi.org/10.1007/s11192-012-0811-9
CR  - Chen, F.C., and Wang, S.M. (2010). Future Trend and Effect of R&D Expenditure in Science and Technology Toward the Industry Between Taiwan and the United States. Journal of Statistics & Management Systems, 13 (2), 243-254. https:// doi.org/10.1080/09720510.2010.10701467
CR  - Deng, J. L. (1982). Control Problems of Grey System. Systems & Control Letters, 1 (5), 288–294. https://doi.org/10.1016/ S0167-6911(82)80025-X
CR  - Deng, J. L. (1989). Introduction to Grey System Theory, Journal of Grey System, 1 (1), 1-24.
CR  - Es, H. A., Hamzacebi, C., and Firat, S. U. O. (2018). GRA-TRI: A Multicriteria Decision Aid Classification Method Based on Grey Relational Analysis. The Journal of Grey System, 30 (3), 113.
CR  - Feng, Y., Zhang, H., Chiu, Y., and Chang, T.-H. (2021). Innovation Efficiency and the Impact of the Institutional Quality: A Cross-Country Analysis Using the Two-Stage Meta-Frontier Dynamic Network DEA Model. Scientometrics, 126 (4), 3091-3129. https://doi.org/10.1007/s11192-020-03829-3
CR  - Guan, J., and Zuo, K. (2014). A Cross-Country Comparison of Innovation Efficiency. Scientometrics, 100 (2), 541-575. https://doi.org/10.1007/s11192-014-1288-5
CR  - Hsu, L. C. (2011). Using Improved Grey Forecasting Models to Forecast the Output of Opto-Electronics Industry. Expert Systems with Applications, 38 (11), 13879-13885. https://doi. org/10.1016/j.eswa.2011.04.192
CR  - Hu, P.Y. (2004). Using a Grey Multipurpose Decision System for Car Purchasing. Journal of Grey Systems, 7, 11-14.
CR  - Hu, Y. C. (2020). Energy Demand Forecasting Using a Novel Remnant GM (1, 1) Model. Soft Computing, 24 (18), 1390313912. https://doi.org/10.1007/s00500-020-04765-3
CR  - Jian, L.R., and Liu, S.F. (2013). The Restriction Mechanism of Chinese University Results Efficiency Based on the Hybrid of VPRS and Optimized GM(1,1). 11th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2013), 1558, 1718-1725. https://doi.org/10.1063/1.4825860
CR  - Jian, L.R., and Yu, H.Z. (2014). The Restriction Mechanism on R&D Result Transfer Performance for Chinese Universities Based on Grey Incidence Analysis and Optimized GM(1,1). Journal of Grey System, 26 (3), 12-22.
CR  - Kayacan, E., Ulutas, B. ve Kaynak, O. (2010). Grey System TheoryBased Models in Time Series Prediction. Expert Systems with Applications, 2010, 37, 1784-1789.
CR  - Li, G. D., Yamaguchi, D., and Nagai, M. (2007). Application of GM (1, 1)-Markov Chain Combined Model to China’s Automobile Industry. International Journal of Industrial and Systems Engineering, 2 (3), 327-347.
CR  - Li, G.D., Masuda, S., Wang, C.H., and Nagai, M. (2010). The Hybrid Grey-Based Model for Cumulative Curve Prediction in Manufacturing System. The International Journal of Advanced Manufacturing Technology, 47, 337-349.
CR  - Li, L., Wang, R.X., and Li, X.C. (2017). Grey GM(1,1,βk) Model and Its Application in R&D Personnel. Journal of Grey System, 29 (1), 120-134.
CR  - Lin, Y., and Liu S. (2004). A Historical Introduction to Grey Systems Theory. 2004 IEEE International Conference on Systems, Man and Cybernetics, 3, 2403-2408. https://doi. org/10.1109/ICSMC.2004.1400689
CR  - Liu, S., and Lin, Y. (2010). Grey Information: Theory and Practical Applications. SpringerVerlag, Berlin.
CR  - Liu, S., Yang, Y., and Forrest, J. (2017). Grey Data Analysis. Springer Singapore. https://doi.org/10.1007/978-981-101841-1
CR  - Miao, C. L., D. B. Fang, L. Y. Sun, and Q. L. Luo. (2017). Natural Resources Utilization Efficiency under the Influence of Green Technological Innovation. Resources, Conservation & Recycling, 126, 153-161. https://doi.org/10.1016/j. resconrec.2017.07.019
CR  - Minniti, M. (2008), The Role of Government Policy on Entrepreneurial Activity: Productive, Unproductive, or Destructive?, Entrepreneurship Theory and Practice, 32 (5), 779-790. https://doi.org/10.1111/j.1540-6520.2008.00255.x
CR  - Rowley, C., and I. Oh. 2020. Trends in Chinese Management and Business: Change, Confucianism, Leadership, Knowledge & Innovation. Asia Pacific Business Review, 26 (1), 1-8. https:// doi.org/10.1080/13602381.2019.1698707
CR  - Shen, C.G., Yu, L.B., and Wei, Y.G. (2010). Grey Interval Prediction of Regional Scientific and Technological Human Resource Input Based on GM(1,1). ETP/IITA Conference on System Science and Simulation in Engineering (SSSE 2010), 267-270.
CR  - Tirkel, I. (2013). Forecasting Flow Time in Semiconductor Manufacturing Using Knowledge Discovery in Databases. International Journal of Production Research, 51 (18), 55365548. https://doi.org/10.1080/00207543.2013.787168
CR  - TÜİK (Türkiye İstatistik Kurumu) (2025). Bölgesel İstatistikler. https://biruni.tuik. gov.tr/bolgeselistatistik/tabloYilSutunGetir. do?durum=yillariGetir&menuNo=436&altMenuGoster= 0&tabloNo=294, (Erişim Tarihi: 27.02.2025).
CR  - Wang Z., Yongbo, Z., and Huimin, F. (2014). Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size. Mathematical Problems in Engineering, http://dx.doi.org/10.1155/2014/572173.
CR  - Wang, C. N., and Le, A. P. (2019). Application of Multi-Criteria Decision-Making Model and GM (1, 1) Theory for Evaluating Efficiency of FDI on Economic Growth: A Case Study in Developing Countries. Sustainability, 11 (8), 2389. https://doi.org/10.3390/su11082389
CR  - Wang, C.-N., Dang, T.-T., Nguyen, N.-A.-T., and Le, T.-T.-H. (2020). Supporting Better Decision-Making: A Combined Grey Model and Data Envelopment Analysis for Efficiency Evaluation in E-Commerce Marketplaces. Sustainability, 12 (24), 10385. https://doi.org/10.3390/su122410385
CR  - Wang, C.-N., Nguyen, T.-L., and Dang, T.-T. (2021). Analyzing Operational Efficiency in Real Estate Companies: an Application of GM (1,1) and DEA Malmquist Model. Mathematics, 9, 202. https://doi.org/10.3390/math9030202
CR  - Wang, S., Fan, J., Zhao, D., and Wang, S. (2016). Regional Innovation Environment and Innovation Efficiency: The Chinese Case, Technology Analysis and Strategic Management, 28 (4), 396-410. https://doi.org/10.1080/0953 7325.2015.1095291
CR  - Wang, Z. X., Wang, Z. W., and Li, Q. (2020). Forecasting the Industrial Solar Energy Consumption Using a Novel Seasonal GM (1, 1) Model with Dynamic Seasonal Adjustment Factors. Energy, 200, 117460. https://doi.org/10.1007/ s00500-020-04765-3
CR  - Wei, Q., Chen, M., and Ruan, C.Y. (2021). Research and Development Investment Combination Forecasting Model of High-Tech Enterprises Based on Uncertain Information. Mathematical Problems in Engineering, 2021, 6684711. https://doi.org/10.1155/2021/6684711
CR  - WIPO (2024). The Global Innovation Index 2024: Unlocking the Promise of Social Entrepreneurship, Geneva, Switzerland.
CR  - Wu, J., Zhuo, S., and Wu, Z. (2017). National Innovation System, Social Entrepreneurship, and Rural Economic Growth in China. Technological Forecasting and Social Change, 121, 238-250. https://doi.org/10.1016/j.techfore.2016.10.014
CR  - Xia, H.X. Jiao, J.Y. Wang, P.C. Tang, X.W. Xiong, C.Y., and Wu, L.S. (2024). Research on the Corporate Innovation Resilience of China Based on FGM(1,1) and Fuzzy-Set Qualitative Comparative Analysis Model. Fractal and Fractional, 8 (1), 2. https://doi.org/10.3390/fractalfract8010002
CR  - Xiao, J.X. Liao, Y. Hou, R.Y., Peng, W.H., and Dan, H.J. (2024). Evaluation and Prediction of Regional Innovation Ecosystem from the Perspective of Ecological Niche: Nine Cities in Hubei Province, China as the Cases. Sustainability, 16 (11), 4489. https://doi.org/10.3390/su16114489
CR  - Xie, X.M., Liu, X.J., and Blanco, C. (2023). Evaluating and Forecasting the Niche Fitness of Regional Innovation Ecosystems: A Comparative Evaluation of Different Optimized Grey Models. Technological Forecasting and Social Change, 191, 122473. https://doi.org/10.1016/j. techfore.2023.122473
CR  - Yu, Z., Yang, C., Zhang, Z., and Jiao, J. (2015). Error Correction Method Based on Data Transformational GM (1, 1) and Application on Tax Forecasting. Applied Soft Computing, 37, 554-560. https://doi.org/10.1016/j.asoc.2015.09.001
CR  - Yuan, C., Liu, S., and Fang, Z. (2016). Comparison of China’s Primary Energy Consumption Forecasting by Using ARIMA (The Autoregressive Integrated Moving Average) Model and GM (1, 1) Model. Energy, 100, 384-390. https://doi. org/10.1016/j.energy.2016.02.001
CR  - Zhang, Y., Xu, Y., and Wang, Z. (2009). GM(1,1) Grey Prediction of Lorenz Chaotic System. Chaos, Solitons & Fractals, 42 (2), 1003–1009. https://doi.org/10.1016/j.chaos.2009.02.031
CR  - Zhao, Z., Wang, J., Zhao, J., and Su, Z. (2012). Using a Grey Model Optimized by Differential Evolution Algorithm to Forecast the per Capita Annual Net Income of Rural Households in China. Omega, 40 (5), 525-532. https://doi.org/10.1016/j. omega.2011.10.003
UR  - https://doi.org/10.61138/bolgeselkalkinmadergisi.1652668
L1  - https://dergipark.org.tr/tr/download/article-file/4667273
ER  -