Türkiye Can Leverage the Power of Innovation, Artificial Intelligence, and Fintech to Enhance Productivity

Öz

The aim of this study is to reveal what Türkiye should do to increase its economic and social productivity according to the GII, fintech index, and AI index analyses. Based on the total scores of the GII, fintech, and AI indices were analyzed with K-Means clustering algorithm and linear projection methods. Türkiye needs to make a breakthrough in areas such as fintech and artificial intelligence, which are at the beginning of the digital age. In particular, it needs to focus on innovation and imitation, which can be combined into a new term, "imovation." Türkiye needs to increase its production of innovative products on a global scale. Focusing on and improving its competencies in this field is critical for economic and social productivity. Unlike previous studies, this study has been analyzed by selecting 51 countries that include all three of the Global Innovation, Fintech and AI indices. Based on these indices, Türkiye has been compared with other countries and what Türkiye needs to do for economic and social productivity has been revealed.

Anahtar Kelimeler

• Global Innovation Index
• Fintech Index
• AI Index
• Productivity

Türkiye Verimliliği Artırmak için İnovasyon, Yapay Zeka ve Fintech'in Gücünden Yararlanabilir

Öz

Bu çalışmanın amacı, GII, fintek endeksi ve yapay zekâ endeksi analizlerine göre Türkiye'nin ekonomik ve sosyal verimliliğini artırmak için neler yapması gerektiğini ortaya koymaktır. GII, fintek ve yapay zekâ endekslerinin toplam puanları baz alınarak K-Means kümeleme algoritması ve doğrusal projeksiyon yöntemleri ile analiz edilmiştir. Türkiye'nin dijital çağın başlangıcında olan fintek ve yapay zekâ gibi alanlarda atılım yapması gerekmektedir. Özellikle, yeni bir terim olan "imovasyon" ile birleştirilebilecek inovasyon ve taklit üzerine odaklanmalıdır. Türkiye'nin küresel ölçekte yenilikçi ürün üretimini artırması gerekmektedir. Bu alandaki yetkinliklerine odaklanması ve bunları geliştirmesi ekonomik ve sosyal üretkenlik açısından kritik önem taşımaktadır. Bu çalışma, önceki çalışmalardan farklı olarak Küresel İnovasyon, Fintech ve Yapay Zekâ endekslerinin üçünü de içeren 51 ülke seçilerek analiz edilmiştir. Bu endeksler baz alınarak Türkiye diğer ülkelerle karşılaştırılmış ve Türkiye'nin ekonomik ve sosyal üretkenlik için yapması gerekenler ortaya konulmuştur.

Anahtar Kelimeler

• Küresel İnovasyon Endeksi
• Fintech Endeksi
• Yapay Zeka Endeksi
• Verimlilik
• Türkiye

Kaynakça

1. Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor (w25684; s. w25684). National Bureau of Economic Research. https://doi.org/10.3386/w25684
2. Aghion, P., Bloom, N., Blundell, R., Griffith, R., & Howitt, P. (2002). Competition and innovation: An inverted u relationship (w9269; s. w9269). National Bureau of Economic Research. https://doi.org/10.3386/w9269
3. Ahmad, S. A., & Mamun, A. A. (2020). Opportunities of Islamic FinTech: The Case of Bangladesh and Türkiye. Cenraps Journal of Social Sciences. https://doi.org/10.46291/cenraps.v2i3.39
4. AI Index Steering Committee. (2023). Artificial Intelligence Index Report 2023 Introduction to the AI Index Report 2023.
5. Anagnostopoulos, I. (2018). Fintech and Regtech: Impact on Regulators and Banks. Journal of Economics and Business. https://doi.org/10.1016/j.jeconbus.2018.07.003
6. Arner, D. W., Barberis, J., & Buckley, R. P. (2015). The Evolution of Fintech: A New Post-Crisis Paradigm? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2676553
7. Autor, D. H. (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives. https://doi.org/10.1257/jep.29.3.3
8. Baregheh, A., Rowley, J., & Sambrook, S. (2009). Towards a multidisciplinary definition of innovation. Management Decision, 47(8), 1323-1339. https://doi.org/10.1108/00251740910984578

9. Bayram, O., Talay, I., & Feridun, M. (2022). Can Fintech Promote Sustainable Finance? Policy Lessons From the Case of Türkiye. Sustainability. https://doi.org/10.3390/su141912414
10. Bessen, J. (2018). AI and Jobs: The role of demand. National Bureau of Economic Research. https://doi.org/10.3386/w24235
11. Bloom, N., Garicano, L., Sadun, R., & Reenen, J. V. (2014). The Distinct Effects of Information Technology and Communication Technology on Firm Organization. Management Science. https://doi.org/10.1287/mnsc.2014.2013
12. Bloom, N., Jones, C., Van Reenen, J., & Webb, M. (2017). Are ideas getting harder to find? (w23782; s. w23782). National Bureau of Economic Research. https://doi.org/10.3386/w23782
13. Boulesteix, A.-L., & Strimmer, K. (2006). Partial least squares: A versatile tool for the analysis of high-dimensional genomic data. Briefings in Bioinformatics, 8(1), 32-44. https://doi.org/10.1093/bib/bbl016
14. Çakar, N. D., & Ertürk, A. (2010). Comparing Innovation Capability of Small and Medium-Sized Enterprises: Examining the Effects of Organizational Culture and Empowerment. Journal of Small Business Management. https://doi.org/10.1111/j.1540-627x.2010.00297.x
15. Calisir, F., Gumussoy, C. A., & Guzelsoy, E. (2013). Impacts of Learning Orientation on Product Innovation Performance. The Learning Organization. https://doi.org/10.1108/09696471311328442
16. Cao, L., Yang, Q., & Yu, P. S. (2021). Data Science and AI in FinTech: An Overview. International Journal of Data Science and Analytics. https://doi.org/10.1007/s41060-021-00278-w
17. Çınar, Ö., Altuntas, S., & Alan, M. A. (2020). Technology Transfer and Its Impact on Innovation and Firm Performance: Empirical Evidence from Turkish Export Companies. Kybernetes. https://doi.org/10.1108/k-12-2019-0828
18. Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., & Zupan, B. (2013). Orange: Data mining toolbox in python. Journal of Machine Learning Research, 14(71), 2349-2353. http://jmlr.org/papers/v14/demsar13a.html
19. UST-Global, Inc., & Donepudi, P. K. (2017). Machine learning and artificial intelligence in banking. Engineering International, 5(2), 83-86. https://doi.org/10.18034/ei.v5i2.490
20. Dubé, A. K., & Wen, R. (2021). Identification and Evaluation of Technology Trends in K-12 Education From 2011 to 2021. Education and Information Technologies. https://doi.org/10.1007/s10639-021-10689-8
21. Dwivedi, P., Alabdooli, J. I., & Dwivedi, R. (2021). Role of FinTech Adoption for Competitiveness and Performance of the Bank: A Study of Banking Industry in UAE. International Journal of Global Business and Competitiveness. https://doi.org/10.1007/s42943-021-00033-9
22. Farahani, M. S., Esfahani, A., Falatouri Moghaddam, M. N., & Ramezani, A. (2022). The Impact of Fintech and Artificial Intelligence on COVID 19 and Sustainable Development Goals. International Journal of Innovation in Management Economics and Social Sciences. https://doi.org/10.52547/ijimes.2.3.14
23. Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society Series C (Applied Statistics). https://doi.org/10.2307/2346830
24. Hussein Kassim, A., McGuire MyMy, J., Lyons, A., Puah, K. S., Bhaskar, S., Chivunga, M. N., Arieli, I., More, N., Biedermann, J., & Olivera, S. (2021). Global Fintech Rankings Report Bridging the Gap.
25. Jiang, S., Tang, Y., & Lee, J. C. (2022). A preliminary study exploring the effects of artificial intelligence on fintech innovation resistance. Içinde Y. Jiang, Y. Shvets, & H. Mallick (Ed.), Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022) (C. 225, ss. 923-927). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-036-7_136
26. Kaynak, S., Altuntas, S., & Dereli, T. (2017). Comparing the innovation performance of EU candidate countries: An entropy-based TOPSIS approach. Economic Research-Ekonomska Istraživanja, 30(1), 31-54. https://doi.org/10.1080/1331677X.2016.1265895
27. Kazachenok, O. P., Stankevich, G. V., Chubaeva, N. N., & Tyurina, Y. G. (2023). Economic and legal approaches to the humanization of FinTech in the economy of artificial intelligence through the integration of blockchain into ESG Finance. Humanities and Social Sciences Communications, 10(1), 167. https://doi.org/10.1057/s41599-023-01652-8
28. Koren, Y., & Carmel, L. (2003). Visualization of labeled data using linear transformations. IEEE Symposium on Information Visualization 2003 (IEEE Cat. No.03TH8714), 121-128. https://doi.org/10.1109/INFVIS.2003.1249017
29. Leban, G., Zupan, B., Vidmar, G., & Bratko, I. (2006). Vizrank: Data visualization guided by machine learning. Data Mining and Knowledge Discovery, 13(2), 119-136. https://doi.org/10.1007/s10618-005-0031-5
30. Li, J., Li, N., & Cheng, X. (2021). The impact of fintech on corporate technology innovation based on driving effects, mechanism identification, and heterogeneity analysis. Discrete Dynamics in Nature and Society, 2021, 1-12. https://doi.org/10.1155/2021/7825120
31. Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. https://doi.org/10.1109/TIT.1982.1056489
32. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236
33. Naranjo‐Valencia, J. C., Jimenez‐Jimenez, D., & Sanz‐Valle, R. (2017). Organizational culture and radical innovation: Does innovative behavior mediate this relationship? Creativity and Innovation Management, 26(4), 407-417. https://doi.org/10.1111/caim.12236
34. Onderco, M., & Zutt, M. (2021). Emerging technology and nuclear security: What does the wisdom of the crowd tell us? Contemporary Security Policy, 42(3), 286-311. https://doi.org/10.1080/13523260.2021.1928963
35. Öztürk, R., & Kula, V. (2021). A general profile of artificial intelligence adoption in banking sector: A survey of banks in afyonkarahisar province of turkey. Journal of corporate governance, insurance and risk management, 8(2), 146-157. https://doi.org/10.51410/jcgirm.8.2.10
36. Pătraşcu, P. (2021). Emerging technologies and national security: The impact of iot in critical infrastructures protection and defence sector. Land Forces Academy Review, 26(4), 423-429. https://doi.org/10.2478/raft-2021-0055
37. Prabhaker, P. R., Goldhar, J. D., & Lei, D. (1995). Marketing implications of newer manufacturing technologies. Journal of Business & Industrial Marketing, 10(2), 48-58. https://doi.org/10.1108/08858629510087373
38. Quintane, E., Mitch Casselman, R., Sebastian Reiche, B., & Nylund, P. A. (2011). Innovation as a knowledge‐based outcome. Journal of Knowledge Management, 15(6), 928-947. https://doi.org/10.1108/13673271111179299
39. Raban, Y., & Hauptman, A. (2018). Foresight of cyber security threat drivers and affecting technologies. Foresight, 20(4), 353-363. https://doi.org/10.1108/FS-02-2018-0020
40. Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323-2326. https://doi.org/10.1126/science.290.5500.2323
41. Shin, Y. J., & Choi, Y. (2019). Feasibility of the fintech industry as an innovation platform for sustainable economic growth in korea. Sustainability, 11(19), 5351. https://doi.org/10.3390/su11195351
42. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489. https://doi.org/10.1038/nature16961
43. Steinley, Douglas. (2006). K‐means clustering: A half‐century synthesis. British Journal of Mathematical and Statistical Psychology, 59(1), 1-34. https://doi.org/10.1348/000711005X48266
44. Sun, Y., Ying, L., & Zhang, J. (2022). Analysis of the impact of fintech on small and medium-sized enterprises: 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022), Zhuhai, China. https://doi.org/10.2991/aebmr.k.220307.132
45. Tekin, E., Ramadani, V., & Dana, L.-P. (2021). Entrepreneurship in Turkey and other Balkan countries: Are there opportunities for mutual co-operation through internationalisation? Review of International Business and Strategy, 31(2), 297-314. https://doi.org/10.1108/RIBS-10-2020-0133
46. Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319-2323. https://doi.org/10.1126/science.290.5500.2319
47. T., G., & C., A. (2015). Futuristic computational technologies: A scenario analysis. International Journal of Computer Applications, 127(16), 15-21. https://doi.org/10.5120/ijca2015906687
48. Toplak, M., Birarda, G., Read, S., Sandt, C., Rosendahl, S. M., Vaccari, L., Demšar, J., & Borondics, F. (2017). Infrared orange: Connecting hyperspectral data with machine learning. Synchrotron Radiation News, 30(4), 40-45. https://doi.org/10.1080/08940886.2017.1338424
49. Toplak, M., Read, S. T., Sandt, C., & Borondics, F. (2021). Quasar: Easy machine learning for biospectroscopy. Cells, 10(9), 2300. https://doi.org/10.3390/cells10092300
50. Wali, A. Z., & Popal, A. W. (2020). The emerging issues and impacts of technology in classroom learning. International Journal of Emerging Technologies in Learning (iJET), 15(15), 237. https://doi.org/10.3991/ijet.v15i15.14175
51. Dutta, S., Lanvin, B., Wunsch-Vincent, S., León, L. R., & World Intellectual Property Organization. (t.y.). Global innovation index 2022: (Subtitle) /. Unknown. https://doi.org/10.34667/TIND.46596
52. Yeniaras, V., Kaya, I., & Ashill, N. (2020). The effects of social ties on innovation behavior and new product performance in emerging economies: Evidence from Turkey. Journal of Business & Industrial Marketing, 35(4), 699-719. https://doi.org/10.1108/JBIM-12-2018-0371