SavedAdd your journal

Journals / Karaelmas Fen ve Mühendislik Dergisi / 2026 / Cilt: 16 Sayı: 2

The Association Between Artificial Intelligence Adoption, Trade Facilitation, and Estimated Trade Cost Reduction Potential: A Regional Qualitative-Quantitative Exploratory Analysis

Yapay Zeka Benimsenmesi, Ticaretin Kolaylaştırılması ve Tahmini Ticaret Maliyeti Azaltma Potansiyeli Arasındaki İlişki: Bölgesel Bir Keşif Analizi

Hakan Kaya(Kestel Belediyesi)0000-0002-0812-4839
Pages
21–33
Publication Date
2026-07-24
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results are therefore not to be taken as evidence of inference for regression or for effect sizes but only for descriptive patterns.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This qualitative–quantitative exploratory analysis investigates descriptive patterns linking regional levels of artificial intelligence adoption, trade facilitation implementation, and estimated potentials for reduction in international trade costs.
📊
Key Findings & Data:By using regional aggregates (N=7) from the UN Global Survey on Digital and Sustainable Trade Facilitation (2025) and McKinsey's State of Artifical Intellegence Report (2025), ridge regression analysis is used solely with the purpose of stabilizing numerical values due to high multicollinearity Variance Inflation Factor (VIF) > 120.
💡
Conclusion & Impact:The results are therefore not to be taken as evidence of inference for regression or for effect sizes but only for descriptive patterns.
🔬Design:Survey & Empirical Study
📍Scope:2025 Verileri
⚙️Variables:Yapay Zekâ · Dijital Uçurum · Küçük Örneklem Sınırlamaları

Abstract

This qualitative–quantitative exploratory analysis investigates descriptive patterns linking regional levels of artificial intelligence adoption, trade facilitation implementation, and estimated potentials for reduction in international trade costs. By using regional aggregates (N=7) from the UN Global Survey on Digital and Sustainable Trade Facilitation (2025) and McKinsey's State of Artifical Intellegence Report (2025), ridge regression analysis is used solely with the purpose of stabilizing numerical values due to high multicollinearity Variance Inflation Factor (VIF) > 120. The values derived (β ≈ -0.31 to -0.34) are not interpretable within the context of causality or inferential explanation, but only indicative of certain heuristics that correspond with the law of diminishing returns, especially within the early adopting groups. Considering the extremely small sample size, proxy variables, and cross-sectional nature, all the results are only to be taken as pattern-generating inputs within the formation of hypotheses for future, more robust research. The results are therefore not to be taken as evidence of inference for regression or for effect sizes but only for descriptive patterns.

Özet

Bu keşifsel çalışma, yapay zekâ benimsenmesinin bölgesel düzeyleri, ticaretin kolaylaştırılması uygulamaları ve uluslararası ticaret maliyetlerinde tahmini azalma potansiyeli arasındaki istatistiksel ilişkiyi incelemektedir. BM Dijital ve Sürdürülebilir Ticaretin Kolaylaştırılmasına İlişkin Küresel Anketi (2025) ve McKinsey'in Yapay Zekâ Durumu Raporu'ndan (2025) açık olarak erişilebilen bölgesel toplam verileri (N = 7) kullanarak, sıradan en küçük kareler spesifikasyonlarında gözlemlenen ciddi çoklu doğrusallığı (VIF> 120) azaltmak için ridge regresyon modelleri tahmin edilmektedir. Bulgular, mevcut veri sınırları içinde, yapay zekâ benimseme düzeyleri ile kalan ticaret maliyeti azaltma potansiyeli arasında gözlemlenebilen betimsel bir eğilimi yansıtmaktadır (β ≈ −0,31 ila −0,34). Bu, erken benimseyenlerin verimlilik artışının büyük bir kısmını zaten elde etmiş oldukları hipoteziyle tutarlıdır. Örneklem büyüklüğünün oldukça küçük olması, yüksek çoklu doğrusallık, aşırı öğrenme riskleri, endojenite endişeleri ve proxy tabanlı değişken inşası gibi faktörler göz önünde bulundurulduğunda, bulguların nedensel, yönlü veya genelleştirilebilir olmaktan ziyade, daha çok öncü ve çıkarım dışı paternler olarak değerlendirilmesi niteliğinde yorumlanmalıdır ve ileride yapılacak daha kapsamlı araştırmalar için hipotez oluşturma amacı taşımaktadır.

🔖

Cite This Article

Kaya, H., *, H. K. (2026). The Association Between Artificial Intelligence Adoption, Trade Facilitation, and Estimated Trade Cost Reduction Potential: A Regional Qualitative-Quantitative Exploratory Analysis. Karaelmas Fen ve Mühendislik Dergisi, Cilt: 16 Sayı: 2, 21–33. https://doi.org/10.7212/karaelmasfen.1814635

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (47)

  1. Achar, S. (2019). Early consequences regarding the impact of artificial intelligence on international trade. American Journal of Trade and Policy, 6(3), 119-126. Doi: 10.18034/ajtp.v6i3.634
  2. Agrawal, A., Gans, J., & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labor market impact of automating prediction. Journal of Economic Perspectives, 33(2), 31–50. Doi: 10.1257/jep.33.2.31
  3. Ahmedov, I. (2020). The impact of digital economy on international trade. European Journal of Business and Management Research, 5(4). Doi: 10.24018/ejbmr.2020.5.4.389
  4. Aly, H. (2022). Digital transformation, development and productivity in developing countries: is artificial intelligence a curse or a blessing?. Review of Economics and Political Science, 7(4), 238-256. Doi: 10.1108/REPS-11-2019-0145
  5. Aslam, M. F., Wang, B., & Hou, Y. (2024). Artificial intelligence and international trade laws: in light of trade policy and data sharing. Business Law Review, 45(2), 48-55. Doi: 10.54648/bula2024007
  6. Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital Business Strategy: Toward a Next Generation of Insights. MIS Quarterly, 37(2), 471–482. Available at: http://www.jstor.org/stable/43825919
  7. Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International journal of information management, 57, 102225. Doi: 10.1016/j.ijinfomgt.2020.102225
  8. Burri, M. (2021). Big data and global trade law. Cambridge University Press. Doi: 10.1017/9781108919234
  9. Ciuriak, D., & Ptaškina, M. (2018). The digital transformation and the transformation of international trade. RTA Exchange. Geneva, Switzerland: ICTSD. Available at: http://hdl.handle.net/11159/1651
  10. Collins, D. 2023. The liberalization of digital trade by the WTO: opportunities and challenges for SMEs. The Elgar Companion to the World Trade Organization, 51-68. Doi: 10.4337/9781800882867.00011
  11. Dong, Y., & Liu, J. (2023). Impact of trade facilitation on cross-border e-commerce: A perspective from China. In Proceedings of the 2023 14th International Conference on E-business, Management and Economics, pp. 38-44. Doi: 10.1145/3616712.3616778 Duval, Y., & Utoktham, C. (2024). Impact of disaggregated trade facilitation indicators and individual measures on trade using a structural gravity model. Available at: https://hdl.handle.net/20.500.12870/7403
  12. European Commission EU-Mercosur Agreement. (2024). Available at: https://policy.trade.ec.europa.eu/eu-trade-relationships-country-and-region/countries-and-regions/mercosur/eu-mercosur-agreement_en
  13. Ferencz, J., González, J. L., & García, I. O. (2022). Artificial Intelligence and international trade: Some preliminary implications. Available at: https://read.oecd-ilibrary.org/content/dam/oecd/en/publications/reports/2022/04/artificial-intelligence-and-international-trade_9034b5f2/13212d3e-en.pdf
  14. Ganne, E., Locks, L., & Xu, A. (2024). Trading with Intelligence: How AI Shapes and is Shaped by International Trade. Available at: https://coilink.org/20.500.12592/17dit14
  15. Goldfarb, A., & Trefler, D. (2018). AI and international trade (No. w24254). National Bureau of Economic Research. Doi: 10.3386/w24254
  16. GSMA. (2025). The mobile economy 2025. Available at: https://www.gsma.com/mobileeconomy/
  17. Holmström, J. (2022). From AI to digital transformation: The AI readiness framework. Business horizons, 65(3), 329-339. Doi: 10.1016/j.bushor.2021.03.006
  18. International Telecommunication Union (ITU). (2025). Measuring digital development: Facts and figures 2025 (ITU Digital Development Dashboard). Available at: https://www.itu.int/en/ITU-D/Statistics/Pages/facts/default.aspx
  19. Ismail, N. W. (2021). Digital trade facilitation and bilateral trade in selected Asian countries. Studies in Economics and Finance, 38(2), 257-271. Doi: 10.1108/SEF-10-2019-0406
  20. Joint Research Centre. (2008). Handbook on constructing composite indicators: methodology and user guide. OECD publishing.
  21. Jones, E. (2023). Digital disruption: artificial intelligence and international trade policy. Oxford Review of Economic Policy, 39(1), 70-84. Doi: 10.1093/oxrep/grac049
  22. Kar, S., Kar, A. K., & Gupta, M. P. (2021). Modeling drivers and barriers of artificial intelligence adoption: Insights from a strategic management perspective. Intelligent Systems in Accounting, Finance and Management, 28(4), 217-238. Doi: 10.1002/isaf.1503
  23. Kere, S., & Zongo, A. (2023). Digital technologies and intra-African trade. International Economics, 173, 359-383. Doi: 10.1016/j.inteco.2023.01.005
  24. Khan, A. (2024). The intersection of artificial intelligence and international trade laws: Challenges and opportunities. IIUMLJ, 32, 103. Doi: 10.31436/iiumlj.v32i1.912
  25. Kitsios, F., & Kamariotou, M. (2021). Artificial intelligence and business strategy towards digital transformation: A research agenda. Sustainability, 13(4), 2025. Doi: 10.3390/su13042025
  26. Kraus, S., Jones, P., Kailer, N., Weinmann, A., Chaparro-Banegas, N., & Roig-Tierno, N. (2021). Digital Transformation: An Overview of the Current State of the Art of Research. SAGE Open, 11(3). Doi: 10.1177/21582440211047576
  27. Lei, W. C., Toshevska-Trpchevska, K., & Stojkoski, V. (2024). Digital and Sustainable Trade Facilitation in ASEAN Countries. In Asia-Pacific Conference on Economics & Finance (pp. 53-73). Singapore: Springer Nature Singapore. Doi: 10.1007/978-981-96-6998-1_4
  28. McKinsey The State of AI. (2025). Available at: https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai/2025/the-state-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf
  29. Meltzer, J. P. (2019). Governing digital trade. World Trade Review, 18(S1), S23-S48. Doi: 10.1017/S1474745618000502
  30. Menzies, J., Sabert, B., Hassan, R., & Mensah, P. K. (2024). Artificial intelligence for international business: Its use, challenges, and suggestions for future research and practice. Thunderbird International Business Review, 66(2), 185-200. Doi: 10.1002/tie.22370
  31. Mirzaye, S., & Mohiuddin, M. (2025). Digital Transformation in International Trade: Opportunities, Challenges, and Policy Implications. Journal of Risk and Financial Management, 18(8), 421. Doi:10.3390/jrfm18080421
  32. Muhammad, S. S., Dey, B. L., Kamal, M. M., Samuel, L., & Alzeiby, E. A. (2025). Digital transformation or digital divide? Smes' use of AI during global crisis. Technological Forecasting and Social Change, 217, 124184. Doi: 10.1016/j.techfore.2025.124184
  33. OECD/BCG/INSEAD. (2025). The Adoption of Artificial Intelligence in Firms: New Evidence for Policymaking, OECD Publishing, Paris. Doi: 10.1787/f9ef33c3-en
  34. Ouyang, W., & Park, I. (2024). Estimating the Extensive Margin of Exports in the Chinese Market: The Impact of Digital Trade Facilitation on Export Diversification. International Area Studies Review, 27(2), 95-121. Doi: 10.69473/iasr.2024.27.2.95
  35. Ozturk, O. (2024). The impact of AI on international trade: Opportunities and challenges. Economies, 12(11), 298. Doi: 10.3390/economies12110298
  36. Peters, M. A. (2023). Digital trade, digital economy and the digital economy partnership agreement (DEPA). Educational Philosophy and Theory, 55(7), 747-755. Doi: 10.1080/00131857.2022.2041413
  37. Quindimil, M. (2024). Artificial Intelligence and International Trade from a Latin American Perspective. Global Trade and Customs Journal, 19(11/12), 748-756. Doi: 10.54648/gtcj2024084
  38. Shadikhodjaev, S. (2021). Technological neutrality and regulation of digital trade: how far can we go?. European Journal of International Law, 32(4), 1221-1247. Doi: 10.1093/ejil/chab054
  39. Stock, J. H., & Watson, M. W. (2015). Introduction to econometrics (3rd updated edition). Age (X3), 3(0.22).
  40. Tao, H., Wan, Y., Huang, L., & Zhao, Y. (2025). Applications and Challenges of Artificial Intelligence in International Trade and Logistics: Current Status, Future Developments, and Policy Recommendations. International Journal of Computer Science and Information Technology, 5(1), 35-47. Doi:10.5281/zenodo.14915353
  41. Tursunbayeva, A., & Gal, H. C. B. (2024). Adoption of artificial intelligence: A TOP framework-based checklist for digital leaders. Business Horizons, 67(4), 357-368. Doi: 10.1016/j.bushor.2024.04.006
  42. UN Global Survey on Digital and Sustainable Trade Facilitation. (2025). Available at: https://www.untfsurvey.org/files/documents/2025-UNTF-Global-Report.pdf
  43. Vorobiova, V. V., Krupskyi, O. P., & Stasiuk, Y. (2023). The role of digital technologies in modern trade: A study of global trends and prospects for Ukraine. Economic Journal Odessa Polytechnic University 2 (24), 44-55. Doi: 10.15276/EJ.02.2023.5
  44. Wang, S., & Zhu, Y. (2024). An inquiry into the effect of trade facilitation on China's digital product exports to countries along the" Belt and Road". International Review of Economics & Finance, 93, 1246-1259. Doi: 10.1016/j.iref.2024.04.041
  45. Winn, J. K. (2019). Trade Facilitation, Digital Transformation and the Emergence of Global Trade Platforms. Digital Transformation and the Emergence of Global Trade Platforms (June 30, 2019). Doi: 10.2139/ssrn.3412370
  46. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.
  47. World Bank. (2025). World development indicators: GDP (current US$) – Regional aggregates. Available at: https://databank.worldbank.org/source/world-development-indicators