Havacılıkta yapay zekâ ve dijital teknolojiler: Araştırma eğilimleri ve bilgi yapılarına ilişkin bibliyometrik bir analiz

Abstract

Havacılık sektörü, son yıllarda yapay zekâ, büyük veri analitiği ve dijital dönüşüm teknolojilerinin hızla yaygınlaşmasıyla birlikte önemli bir dönüşüm sürecinden geçmektedir. Bu teknolojiler, hava trafik yönetimi, operasyonel planlama, bakım tahminleme ve karar destek sistemleri gibi birçok alanda havacılık sistemlerinin verimliliğini, güvenliğini ve operasyonel performansını artırma potansiyeline sahiptir. Bu çalışma, havacılık alanında yapay zekâ ve dijital teknolojilere ilişkin akademik literatürün gelişimini, kavramsal yapısını ve entelektüel temelini ortaya koymak amacıyla bibliyometrik bir analiz sunmaktadır. Araştırma kapsamında Scopus veri tabanında 2015–2025 yılları arasında yayımlanan ve yapay zekâ, makine öğrenmesi, büyük veri analitiği, dijital dönüşüm, otomasyon, karar destek sistemleri ve blok zincir gibi teknolojileri havacılık bağlamında ele alan toplam 1302 akademik çalışma incelenmiştir. Veri analizi VOSviewer yazılımı kullanılarak gerçekleştirilmiş ve anahtar kelime eş-oluşum analizi, eş-atıf analizi, ortak yazarlık analizi ve bibliyografik eşleştirme analizleri uygulanmıştır. Elde edilen bulgular, literatürün özellikle makine öğrenmesi, derin öğrenme, hava trafik yönetimi ve veri temelli karar destek sistemleri etrafında yoğunlaştığını göstermektedir. Ayrıca araştırma alanının disiplinlerarası bir yapıya sahip olduğu ve havacılık yönetimi, operasyon araştırması, insan faktörleri ve mühendislik disiplinleri arasında güçlü etkileşimler bulunduğu belirlenmiştir. Bibliyografik eşleştirme analizleri ise literatürdeki güncel araştırma cephelerinin veri analitiği, akıllı hava trafik yönetimi ve otonom sistemler gibi temalar etrafında şekillendiğini ortaya koymaktadır. Ayrıca çalışma, havacılıkta yapay zekâ ve dijital teknolojiler literatürünün kavramsal ve entelektüel yapısını sistematik biçimde haritalandırarak gelecekteki araştırmalar için önemli çıkarımlar sunmaktadır.

Keywords

• Havacılık
• yapay zekâ
• makine öğrenmesi
• dijital dönüşüm
• büyük veri analitiği
• bibliyometrik analiz
• VOSviewer

Artificial intelligence and digital technologies in aviation: A bibliometric analysis of research trends and knowledge structures

Abstract

In recent times, the aviation industry has experienced a major change due to a growing use of Artificial Intelligence (AI), big data analytics, and technology for digital transformation. These technologies can improve efficiency, safety, and performance throughout the aviation sector and could be used for many different purposes, including air traffic management, operational planning, predictive maintenance, and decision support systems. To investigate the history, conceptual structure, and intellectual basis of existing academic literature on AI and digital technologies in the aviation industry, this study provides a detailed bibliometric analysis of the existing literature on this issue. This study used existing articles indexed in the Scopus database from 2015 - 2025 as its dataset, which contained 1302 articles about AI, machine learning, big data, digital transformation technologies, automation, decision support systems, and blockchain in an aviation context. The bibliometric analysis was conducted using the VOSviewer software to conduct keyword co-occurrence analysis, co-citation analysis, co-authorship analysis, and bibliography coupling techniques to identify common themes within the existing literature. Based on the analysis undertaken, it was understood that literature predominantly consists of the following themes: machine learning, deep learning, air traffic management, and data-driven decision support systems. Thus, these results demonstrate an interdisciplinary research area with strong relationships between aviation management, operations research, human factors and engineering. The results of the bibliographic coupling analysis also reveal new research areas on data analytics, intelligent air traffic management systems, and autonomous aviation technologies. The findings of this study will assist researchers in understanding how artificial intelligence and digital transformation affect aviation, and it presents a foundation for future studies about these topics by providing a systematic framework for the conceptual and intellectual organization of the field.

Keywords

• Aviation
• artificial intelligence
• machine learning
• digital transformation
• big data analytics
• bibliometric analysis
• VOSviewer

Ethical Statement

This article meets research and publication ethics standards.

References

1. Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), 189. https://doi.org/10.3390/su11010189
2. Ali, N. S., Yu, C., & See, K. F. (2021). Four decades of airline productivity and efficiency studies: A review and bibliometric analysis. Journal of Air Transport Management, 96, 102099. https://doi.org/10.1016/j.jairtraman.2021.102099
3. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
4. Arnott, D., & Pervan, G. (2008). Eight key issues for the decision support systems discipline. Decision Support Systems, 44(3), 657–672. https://doi.org/10.1016/j.dss.2007.09.003
5. Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A., & Verma, S. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research, 333(2–3), 627–652. https://doi.org/10.1007/s10479-021-03956-x
6. Burak, M. F., & Küsbeci, P. (2024). Internet of things and aviation: A bibliometric and visualization analysis. Kybernetes, 53(11), 4502–4521. https://doi.org/10.1108/K-04-2023-0664
7. Dixit, A., & Jakhar, S. K. (2021). Airport capacity management: A review and bibliometric analysis. Journal of Air Transport Management, 91, 102010. https://doi.org/10.1016/j.jairtraman.2020.102010
8. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

9. Dorhetso, S. N., & Quarshie, B. D. (2023). Ratings of driving factors for the adoption and implementation of artificial intelligence in the public sector. Open Access Library Journal, 10, 1–20. https://doi.org/10.4236/oalib.1109982
10. Dou, X. (2020). Big data and smart aviation information management system. Cogent Business & Management, 7(1), 1766736. https://doi.org/10.1080/23311975.2020.1766736
11. Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
12. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925 International Civil Aviation Organization. (2024). Global air navigation plan. https://www.icao.int/sites/default/files/Meetings/a42/Documents/9750_en.pdf
13. Kabashkin, I., Misnevs, B., & Zervina, O. (2023). Artificial intelligence in aviation: New professionals for new technologies. Applied Sciences, 13(21), 11660. https://doi.org/10.3390/app132111660
14. Kahraman, J., & Şahin, D. R. (2025). PESTLE analysis of digitalization in aviation safety. Transport Policy, 178, 103945. https://doi.org/10.1016/j.tranpol.2025.103945
15. Karakaş, S., Üzülmez, M., & Ayvaz, E. E. (2026). Research trends in airport management literature: A bibliometric analysis. Case Studies on Transport Policy, 23, 101737. https://doi.org/10.1016/j.cstp.2026.101737
16. Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. https://doi.org/10.1007/s12599-014-0334-4
17. Li, D., Li, M., Wang, Z., & Li, F. (2026). Toward shared situational awareness in human–AI hybrid intelligence: A survey in high-risk aviation environments. Reliability Engineering & System Safety, 274, 112363. https://doi.org/10.1016/j.ress.2026.112363
18. Li, X., Lai, P. L., Yang, C. C., & Yuen, K. F. (2021). Determinants of blockchain adoption in the aviation industry: Empirical evidence from Korea. Journal of Air Transport Management, 97, 102139. https://doi.org/10.1016/j.jairtraman.2021.102139
19. Lopes, N. M., Aparicio, M., & Neves, F. T. (2025). Challenges and prospects of artificial intelligence in aviation: A bibliometric study. Data Science and Management, 8(2), 207–223. https://doi.org/10.1016/j.dsm.2024.11.001
20. Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261–276. https://doi.org/10.1016/j.jbusres.2019.01.044
21. Narimanidehnavi, M. (2026). Application of data-based artificial intelligence in the aviation industry: A conceptual-analytic review of machine learning and deep learning methods. International Journal of Theoretical & Applied Computational Intelligence, 41–62. https://doi.org/10.65278/IJTACI.2026.3
22. Osman, A., Tahhan, A. A., Younes, T., Ramadan, M., Ghazal, M., & Alkhedher, M. (2026). Battery management in electric aviation: A bibliometric review of technologies, regulations, and international collaborations. Journal of Energy Storage, 142, 119523. https://doi.org/10.1016/j.est.2025.119523
23. Passas, I. (2024). Bibliometric analysis: The main steps. Encyclopedia, 4(2), 1014–1025. https://doi.org/10.3390/encyclopedia4020065
24. Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R., & Carlsson, C. (2002). Past, present, and future of decision support technology. Decision Support Systems, 33(2), 111–126. https://doi.org/10.1016/S0167-9236(01)00139-7
25. Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269. https://doi.org/10.1002/asi.4630240406
26. Trappey, A. J. C., Li, F., Lee, C.-H., Mo, J. P. T., Stjepandic, J., & Jiao, R. (2024). How to manage and balance uncertainty by transdisciplinary engineering methods focusing on digital transformations of complex systems. Advanced Engineering Informatics, 59, 102330. https://doi.org/10.1016/j.aei.2023.102330
27. Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
28. Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
29. Xu, Z., & Saleh, J. H. (2021). Machine learning for reliability engineering and safety applications: Review of current status and future opportunities. Reliability Engineering & System Safety, 211, 107530. https://doi.org/10.1016/j.ress.2021.107530
30. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629