YAPAY ZEKÂ ETKİNLİK ÖLÇÜMÜ: TÜRKİYE’DE ŞİRKETLER ÜZERİNE BİR ARAŞTIRMA

Öz

Endüstri 4.0’a bağlı olarak teknoloji kullanımı her geçen gün artmaktadır. Gerek ülkeler gerekse organizasyonlar küresel rekabet koşullarında rakipleriyle mücadele edebilmek ve sürekli değişen dünyaya uyum sağlayabilmek için yapay zekâ alanına yatırımlar yapmak zorunda kalmıştır. Bir organizasyonun veya ülkenin sürdürülebilirliğini sağlayabilmesi için performansını değerlendirmesi oldukça önemlidir. Veri Zarflama Analizi (VZA) yöntemi performans değerlendirmesinde yaygın bir şekilde kullanılmaktadır. Bu çalışmada Türkiye’nin 2014-2022 yılları arasındaki dokuz yıl için yapay zekâ performansının değerlendirmesi amaçlanmıştır. Araştırmada, karar verme birimi olarak yıllar analize dâhil edilmiştir. Analizlerde iki girdi ve iki çıktı değişkeni kullanılmıştır. Çalışma girdi odaklı CCR VZA ve süper etkinlik modeli kullanılarak gerçekleştirilmiştir. Analiz sonuçlarına göre etkin olan/olmayan karar verme birimleri belirlenmiştir. Etkin olmayan karar verme birimleri için potansiyel iyileştirme önerileri sunulmuştur.

Anahtar Kelimeler

• Endüstri 4.0
• Performans
• Etkinlik
• VZA
• Yapay Zeka

EFFICIENCY MEASUREMENT OF ARTIFICIAL INTELLIGENCE: A RESEARCH ON COMPANIES IN TÜRKİYE

Öz

The use of technology is increasing due to Industry 4.0. Both countries and organizations have had to invest in the field of artificial intelligence (AI) to compete with their rivals in global competitive conditions and to adapt to the ever-changing world. An organization or a country needs to evaluate its performance to ensure its sustainability constantly. The Data Envelopment Analysis (DEA) method is widely used in performance evaluation. This study aimed to evaluate Türkiye AI performance for the nine years between 2014 and 2022. In the research, years were included in the analysis as the decision-making unit. Two input and two output variables were used in the analyses. The study was carried out by using the input-oriented CCR DEA model and its super-efficiency model. According to the results of the analysis, efficient/inefficient decision-making units were determined. Several potential improvement suggestions have been put forward for inefficient decision-making units.

Anahtar Kelimeler

• Industry 4.0
• Performance
• Efficiency
• DEA
• Artificial Intelligence

Kaynakça

1. Antunes, J., Hadi-Vencheh, A., Jamshidi, A., Tan, Y., Wanke, P. (2024). Cost efficiency of Chinese banks: Evidence from DEA and MLP-SSRP analysis. Expert Systems with Applications, 237. https://doi.org/10.1016/j.eswa.2023.121432
2. Arunyanart, S. (2024). Performance evaluation of facility locations using integrated DEA-based techniques. Heliyon, 10, https://doi.org/10.1016/j.heliyon.2024.e32430
3. Aylak, B. L., Oral, O., Yazıcı K. (2021). Using Artificial Intelligence and Machine Learning Applications in Logistics. El-Cezerî Journal of Science and Engineering, 8(1), 74-93. https://doi.org/10.31202/ecjse.776314
4. Azadeh, A., Saberi, M., Moghaddam, R.T., Javanmardi, L. (2011). An integrated Data Envelopment Analysis–
5. Artificial Neural Network–Rough Set Algorithm for assessment of personnel efficiency. Expert Systems with Applications, 38(3), 1364-1373.https://doi.org/10.1016/j.eswa.2010.07.033
6. Banker, R.D., A. Charnes, W.W. Cooper (1984). Models for Estimating Technical and Scale Efficiencies. Management Science, 30(9), 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078
7. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
8. Chintalapati, S., Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38-68. https://doi.org/10.1177/14707853211018428

9. Cooper, W.W., Seiford, L.M., and Zhu, J. (2011). Data envelopment analysis: History, models, and interpretations. In W. Cooper, L. Seiford, & J. Zhu (Eds.), Handbook on data envelopment analysis. Springer
10. Çelik, M.K. (2016). Evaluating the Efficiency of Business in Tourism Sector with Data Envelopment Analysis. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 17, 65-88. https://doi.org/10.18092/ijeas.58275
11. Dalir, O., Torabi, T., Rabiei, M., Jahromi, Y.M. (2024). Application of data envelopment analysis in determining the efficiency of management and company. International Journal of Nonlinear Analysis and Applications (IJNAA), 15(6), 237-243. http://dx.doi.org/10.22075/ijnaa.2023.31175.4582
12. Davenport, T., Guha, A., Grewal, D., Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42. https://doi.org/10.1007/s11747-019-00696-0
13. Dong, Y., Wang, D. (2023). China's artificial intelligence efficiency and its influencing factors: Based on DEA-Malmquist and Tobit regression model. Decision Science Letters, 12(4), 729-738. https://doi.org/10.5267/j.dsl.2023.7.003
14. Emrouznejad, A., Yang, G-L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8. https://doi.org/10.1016/j.seps.2017.01.008 EPRS (2024). https://www.europarl.europa.eu/RegData/etudes/ATAG/2024/760392/EPRS_ATA(2024)760392_EN.pdf Accessed 27.09.2024
15. Ersoy, Y. (2021). Performance Evaluation in Distance Education by Using Data Envelopment Analysis (DEA) and TOPSIS Methods. Arabian Journal for Science and Engineering, 46, 1803-1807. https://doi.org/10.1007/s13369-020-05087-0
16. Ersoy, Y., Tehci, A. (2023). Efficiency Evaluation of Energy Companies with Data Envelopment Analysis. The Journal of International Scientific Researches, 8(3), 360-366. https://doi.org/10.23834/isrjournal.1331147 Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-290. https://doi.org/10.2307/2343100
17. Gao, X., Yang, Z., Sun, Z. (2020). Research on the Innovation Efficiency of Artificial Intelligence Enterprise Based on DEA Method. Advances in Economics, Business and Management Research, Atlantis Press, 133, 1-6. https://doi.org/10.2991/aebmr.k.200402.001
18. Gür, Y. E., Ayden, C., & Yücel, A. (2019). Effects to Human Resources Managements of Developments in Artificial Intelligence. Firat University International Journal of Economics and Administrative Sciences, 3(2), 137-158.
19. Hu, J., Nian, Z., & Wang, X. (2019). Research on financial performance evaluation on artificial intelligence listed companies in China based on DEA method. 2019 Portland International Conference on Management of Engineering and Technology (PICMET), 1-6, IEEE. https://doi.org/10.23919/PICMET.2019.8893931
20. Huang, M. H., Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172. https://doi.org/10.1177/1094670517752459
21. Huang, M. H., Rust, R. T. (2022). A framework for collaborative artificial intelligence in marketing. Journal of Retailing, 98(2), 209-223. https://doi.org/10.1016/j.jretai.2021.03.001
22. IBM (2024). https://www.ibm.com/topics/artificial-intelligence Accessed 12.07.2024
23. İTOSAM, (2024). Akıllı Otomasyon Çağında Ulusların Rekabeti: Yapay, Zeka, Robotlar ve Gelişen Ülkeler, İTO Sektörel Araştırmalar Yayın NO: 2024-18, İstanbul. 1-94. https://itosam.org.tr/duyuru/itosamdan-yeni-rapor-pdtv, Accessed 12/06/2024
24. İşler, B., & Kılıç, M.Y (2021). The Use and Development of Artificial Intelligence in Education 5(1), 1-11. https://doi.org/10.17932/IAU.EJNM.25480200.2021/ejnm_v5i1001
25. Keleş, H. (2022). Artificial Intelligence Applications in Medicine. Journal of Kırıkkale University Faculty of Medicine, 24(3), 604-613. https://doi.org/10.24938/kutfd.1214512
26. Liu, J., Qian, Y., Yang, Y., Yang, Z. (2022). Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China. International Journal of Environmental Resarch and Public Health, 19(4), 1-18. https://doi.org/10.3390/ijerph19042091
27. Lyu, R., & Cui, C. (2024). A Study on the Financial Performance of Chinese Artificial Intelligence Listed Companies Based on the DEA-Malmquist Model. Highlights in Science, Engineering and Technology, 98, 452-462. https://doi.org/10.54097/mrqzrx17
28. Mirmozaffari, M., Shadkam, E., Khalili, S.M., Kabirifar, K., Yazdani, R., Gashteroodkhani, T.A. (2021). A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation. International Journal of Energy Sector Management, 15(3), 523-550. https://doi.org/10.1108/IJESM-02-2020-0003
29. Oukil, A., Kennedy, R.E., Al-Hajri, A., Soltani, A.A. (2024). Unveiling the potential of hotel mergers: A hybrid DEA approach for optimizing sector-wide performance in the hospitality industry. International Journal of Hospitality Management, 116, https://doi.org/10.1016/j.ijhm.2023.103620
30. Öztemel, E. (2020). Yapay Zekâ ve İnsanlığın Geleceği. TÜBA-Bilişim Teknolojileri ve İletişim Çalışma Grubu Bilişim Teknolojisi ve İletişim: İnternet ve Toplumsal Etkileri Çalıştayı, 77-90. https://doi.org/10.53478/TUBA.2020.011
31. Pan, Y., Zhang, C-C., Lee, C-C., Lv, S. (2024). Environmental performance evaluation of electric enterprises during a power crisis: Evidence from DEA methods and AI prediction algorithms. Energy Economics, 130, https://doi.org/10.1016/j.eneco.2023.107285
32. Seiford, L.M, Zhu, J. (1999). Infeasibility of super-efficiency data envelopment analysis models. INFOR: Information Systems and Operational Research, 37(2), 174–187. https://doi.org/10.1080/03155986.1999.11732379
33. Selamzade, F., Ersoy, Y., Ozdemir, Y., Celik, M.Y. (2023). Health Efficiency Measurement of OECD Countries Against the COVID-19 Pandemic by Using DEA and MCDM Methods. Arabian Journal for Science and Engineering, 48(11), 15695-15712. https://doi.org/10.1007/s13369-023-08114-y
34. Shi, J., Mei, J., Zhu, L., Wang, Y. (2024). Estimating the Innovation Efficiency of the Artificial Intelligence Industry in China Based on the Three-Stage DEA Model. IEEE Transactions on Engineering Management, 71, 9217-9228. https://doi.org/10.1109/TEM.2023.3323292
35. TBD, (2020). Türkiye’de Yapay Zekânın Gelişimi için Görüşler ve Öneriler, Türkiye Bilişim Derneği Kavramsal Rapor https://www.tbd.org.tr/pdf/yapay-zeka-raporu.pdf
36. Tsang, Y. P., & Lee, C. K. M. (2022). Artificial intelligence in industrial design: A semi-automated literature survey. Engineering Applications of Artificial Intelligence, 112, 104884. https://doi.org/10.1016/j.engappai.2022.104884
37. TÜİK (2023). https://data.tuik.gov.tr/Bulten/Index?p=Girisimlerde-Bilisim-Teknolojileri-Kullanim-Arastirmasi-2023-49393 Accessed 15.07.2024
38. Xiao, K., Ullah, W., Fu, J., Zhang, X. (2023). Poverty Alleviation Efficiency of Tourism and Its Spatiotemporal Differentiation in Jiangxi Province of China Based on the DEA Model. Sage Open, 13(2). https://doi.org/10.1177/21582440231168835
39. Xu, B., Ouenniche, J. (2012) A data envelopment analysis-based framework for the relative performance evaluation of competing crude oil prices volatility forecasting models. Energy Economics, 34(2), 576–583. https://doi.org/10.1016/j.eneco.2011.12.005
40. Wanke, P., Azad, M. A. K., Barros, C. P. (2016). Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36, 485-498. https://doi.org/10.1016/j.ribaf.2015.10.002
41. Yen, B.T.H., Huang, M-J., Lai, H-J., Cho, H-H., Huang, Y-L. (2023). How smart port design influences port efficiency-A DEA-Tobit approach. Research in Transportation Business & Management, 46, 1-12. https://doi.org/10.1016/j.rtbm.2022.100862
42. Yu, D., He, X. (2020). A bibliometric study for DEA applied to energy efficiency: Trends and future challenges. Applied Energy, 268, https://doi.org/10.1016/j.apenergy.2020.115048
43. Yu, M-M., Rakshit, I. (2023). An alternative assessment approach to global logistics performance evaluation: Common weight H-DEA approach. International Transaction in Operational Research, 1-24. https://doi.org/10.1111/itor.13360
44. Yu, S. (2021). Cloud edge computing for socialization robot based on intelligent data envelopment. Computers & Electrical Engineering, 92, 1-12. https://doi.org/10.1016/j.compeleceng.2021.107136
45. Zhang, Q., Lu, J., Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7(1), 439-457. https://doi.org/10.1007/s40747-020-00212-w
46. Zhang, B., Zhu, J., & Su, H. (2023). Toward the third generation artificial intelligence. Science China Information Sciences, 66(2), 121101. https://doi.org/10.1007/s11432-021-3449-x