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Dijitalleşme İhracat Potansiyelini Nasıl Şekillendiriyor: AB ve Ötesinden Firma Düzeyinde Bulgular

Yıl 2026, Cilt: 11 Sayı: 1 , 179 - 199 , 31.03.2026
https://doi.org/10.30784/epfad.1814381
https://izlik.org/JA96HE84UD

Öz

Bu çalışmanın temel amacı, yapay zeka (YZ) gibi ileri dijital teknolojilerin benimsenmesi ile firmaların ihracat olasılığı arasındaki ilişkiyi probit modeli kullanarak incelemektir. Elde edilen bulgular, yapay zeka, bulut bilişim ve robotik teknolojilerinin benimsenmesinin ihracat yapma olasılığıyla istatistiksel olarak anlamlı ve pozitif bir ilişkiye sahip olduğunu göstermektedir. Büyük veri analitiği, akıllı cihazlar ve blokzinciri teknolojileri ise firmaların ihracat yapma olasılığıyla istatistiksel olarak anlamlı bir ilişki göstermemektedir. Çalışma, ikincil bir amaç olarak, belirli dijital teknolojilerin ihracat yapılan bölgelerle ilişkisini çok değişkenli probit modeli kullanarak analiz etmektedir. Bu yöntem, bölgeye özgü ihracat kararlarında hata terimleri arasındaki olası korelasyonları dikkate almaktadır. Bulgular, yapay zekâ ve akıllı cihazların AB ülkelerine yapılan ihracatla anlamlı biçimde ilişkili olduğunu göstermektedir. Bulut bilişim Çin hariç tüm ülkelere ihracat faaliyetleriyle istatistiksel olarak anlamlı biçimde ilişkilidir. Büyük veri analitiği, AB dışındaki ülkelere yönelik ihracatla anlamlı bir ilişki göstermektedir. Buna karşılık, blokzincir teknolojisinin ihracat bölgeleriyle istatistiksel olarak anlamlı bir ilişkisi bulunmamaktadır. Robotik teknolojiler ise tüm ihracat bölgelerinde ihracat katılımıyla tutarlı ve pozitif bir ilişki sergilemektedir.

Kaynakça

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How Digitalization Shapes Export Potential: Firm-Level Insights From The EU and Beyond

Yıl 2026, Cilt: 11 Sayı: 1 , 179 - 199 , 31.03.2026
https://doi.org/10.30784/epfad.1814381
https://izlik.org/JA96HE84UD

Öz

The primary objective of this study is to examine the relationship between the adoption of advanced digital technologies, such as artificial intelligence (AI), and the probability of exporting, utilizing a probit model. The results indicate that the adoption of artificial intelligence, cloud computing, and robotics technologies is positively and statistically significantly associated with the likelihood of exporting. Big data analytics, smart devices, and blockchain technologies do not show a statistically significant association with firms’ likelihood of exporting. The study investigates the relationship between specific digital technologies and export destinations as a secondary objective by applying a multivariate probit model, which accounts for potential correlations among the error terms across destination-specific export decisions. The findings show that artificial intelligence and smart devices are significantly associated with exports to EU countries. Cloud computingis significantly associated with export activities to all countries except China. Big data analytics is significantly associated with exports to non-EU countries. Incontrast, blockchain technology does not exhibit a statistically significant relationship with export destinations. Robotics technologies exhibit a consistentand positive association with export participation across all export regions.

Kaynakça

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  • Johansson, S. (2009). Market experiences and export decisions in heterogeneous firms (CESIS Electronic Working Paper Series No. 196). Retrieved from https://static.sys.kth.se/itm/wp/cesis/cesiswp196.pdf
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  • Jungmittag, A. and Pesole, A. (2019). The impact of robots on labour productivity: A panel data approach covering 9 industries and 12 countries (JRC Working papers Series on Labour, Education and Technology No. 2019/08). Retrieved from https://www.econstor.eu/bitstream/10419/231332/1/jrc-wplet201908.pdf
  • Kabalci, Y. (2016). A survey on smart metering and smart grid communication. Renewable and Sustainable Energy Reviews, 57, 302-318. https://doi.org/10.1016/j.rser.2015.12.114
  • Kimani, D., Adams, K., Attah-Boakye, R., Ullah, S., Frecknall-Hughes, J. and Kim, J. (2020). Blockchain, business and the fourth industrial revolution: Whence, whither, wherefore and how? Technological Forecasting and Social Change, 161, 120254. https://doi.org/10.1016/j.techfore.2020.120254
  • Kromann, L., Malchow-Møller, N., Skaksen, J.R. and Sørensen, A. (2020). Automation and productivity—a cross-country, cross-industry comparison. Industrial and Corporate Change, 29(2), 265-287. https://doi.org/10.1093/icc/dtz039
  • Krugman, P. (1980). Scale economies, product differentiation, and the pattern of trade. The American Economic Review, 70(5), 950-959. https://www.jstor.org/stable/1805774
  • Kshetri, N. (2011). Cloud Computing in the Global South: Drivers, effects and policy measures. Third World Quarterly, 32(6), 997-1014. https://doi.org/10.1080/01436597.2011.586225
  • Kshetri, N. (2019). Blockchains and international business. IT Professional, 21(4), 8-13. https://doi.org/10.1109/MITP.2019.2909700
  • Lejpras, A. (2019). Determinants of export performance: Differences between service and manufacturing SMEs. Service Business, 13(1), 171-198. https://doi.org/10.1007/s11628-018-0376-7
  • Leloup, L. (2017). Blockchain: La revolution de la confiance. Paris: Editions Eyrolles.
  • Love, J.H., Roper, S. and Zhou, Y. (2016). Experience, age and exporting performance in UK SMEs. International Business Review, 25(4), 806-819. https://doi.org/10.1016/j.ibusrev.2015.10.001
  • Lu, Y., Xu, X. and Wang, L. (2020). Smart manufacturing process and system automation–a critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems, 56, 312-325. https://doi.org/10.1016/j.jmsy.2020.06.010
  • McDaniel, C.A. and Norberg, H.C. (2019). Can blockchain technology facilitate international trade? (Mercatus Research Paper). http://dx.doi.org/10.2139/ssrn.3377708
  • Melitz, M.J. (2003). The impact of trade on intra‐industry reallocations and aggregate industry productivity. Econometrica, 71(6), 1695-1725. https://doi.org/10.1111/1468-0262.00467
  • Meltzer, J.P. (2016). Maximizing the opportunities of the internet for international trade (ICTSD Policy Options Paper). Retrieved from https://www3.weforum.org/docs/E15/WEF_Digital_Trade_report_2015_1401.pdf
  • Mendling, J., Baesens, B., Bernstein, A. and Fellmann, M. (2017). Challenges of smart business process management: An introduction to the special issue. Decision Support Systems, 100, 1-5. https://doi.org/10.1016/j.dss.2017.06.009
  • Morbey, G.K. and Reithner, R.M. (1990). How R&D affects sales growth, productivity and profitability. Research-Technology Management, 33(3), 11-14. https://doi.org/10.1080/08956308.1990.11670656
  • Müller, O. Fay, M. and Vom Brocke, J. (2018). The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. Journal of Management Information Systems, 35(2), 488-509. https://doi.org/10.1080/07421222.2018.1451955
  • Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review 21260. Retrieved from https://assets.pubpub.org/d8wct41f/31611263538139.pdf
  • Nicholas-Donald, A., Mahmood, M.A. and Trevino, L.L. (2018). Does adoption of cloud computing matter? The economic worth of cloud computing implementation. International Journal of Information Systems and Management, 1(4), 328-342. https://doi.org/10.1504/IJISAM.2018.094756
  • Niebel, T., Rasel, F. and Viete, S. (2019). BIG data–BIG gains? Understanding the link between big data analytics and innovation. Economics of Innovation and New Technology, 28(3), 296-316. https://doi.org/10.1080/10438599.2018.1493075
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  • Rammer, C., Czarnitzki, D. and Fernández, G.P. (2021). Artificial intelligence and industrial innovation: Evidence from firm-level data (ZEW-Centre for European Economic Research Discussion Paper No. 21-036). https://dx.doi.org/10.2139/ssrn.3829822
  • Ranjan, P. and Raychaudhuri, J. (2016). The “new-new” trade theory: A review of the literature. In M. Roy and S.S. Roy (Eds.), International trade and international finance explorations of contemporary issues (pp. 3-21). https://doi.org/10.1007/978-81-322-2797-7
  • Ricci, L.A. and Trionfetti, F. (2012). Productivity, networks, and export performance: Evidence from a cross‐country firm dataset. Review of International Economics, 20(3), 552-562. https://doi.org/10.1111/j.1467-9396.2012.01038.x
  • Rodríguez, J.L. and Orellana, B.S. (2020) Human capital and export performance in the Spanish manufacturing firms. Baltic Journal of Management, 15(1), 99-119. https://doi.org/10.1108/BJM-04-2019-0143
  • Rodríguez-Pose, A., Tselios, V., Winkler, D. and Farole, T. (2013). Geography and the determinants of firm exports in Indonesia. World Development, 44, 225-240. https://doi.org/10.1016/j.worlddev.2012.12.002
  • Schniederjans, D.G. and Hales, D.N. (2016). Cloud computing and its impact on economic and environmental performance: A transaction cost economics perspective. Decision Support Systems, 86, 73-82. https://doi.org/10.1016/j.dss.2016.03.009
  • Son, I., Lee, D., Lee, J.N. and Chang, Y.B. (2011). Understanding the impact of IT service innovation on firm performance: The case of cloud computing. Paper presented at the PACIS 2011. Brisbane, Australia. Retrieved from https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1179&context=pacis2011
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  • Swan, M. (2015). Blockchain: Blueprint for a new economy. O'Reilly Media, Inc.
  • Tapscott, D. and Tapscott, A. (2016). Blockchain revolution: How the technology behind bitcoin is changing money, business, and the world. New York: Portfolio/Penguin.
  • Teruel, M., Coad, A., Domnick, C., Flachenecker, F., Harasztosi, P., Janiri, M.L. and Pal, R. (2022). The birth of new HGEs: Internationalization through new digital technologies. The Journal of Technology Transfer, 47(3), 804-845. https://doi.org/10.1007/s10961-021-09875-0
  • Tomiura, E. (2007). Effects of R&D and networking on the export decision of Japanese firms. Research Policy, 36(5), 758-767. https://doi.org/10.1016/j.respol.2007.02.020
  • van Ark, B. (2016). The productivity paradox of the new digital economy. International Productivity Monitor, 31, 3-18. Retrieved from https://research.rug.nl/
  • Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404-409. https://doi.org/10.5465/amd.2018.0084
  • Wang, J. (2020). Big data on the influence of SMEs in export trade financing costs. In J. Maclntyre, J. Zhao and X. Ma (Eds.), 2020 international conference on machine learning and big data analytics for IOT security and privacy (pp. 345-351). https://doi.org/10.1007/978-3-030-62743-0_50
  • Xu, D., Guo, Y. and Huang, M. (2021). Can Artificial intelligence improve firms’ competitiveness during the COVID-19 pandemic: International evidence. Emerging Markets Finance and Trade, 57(10), 1-14. https://doi.org/10.1080/1540496X.2021.1899911
  • Yoon, J., Talluri, S., Yildiz, H. and Sheu, C. (2020). The value of blockchain technology implementation in international trades under demand volatility risk. International Journal of Production Research, 58(7), 2163-2183. https://doi.org/10.1080/00207543.2019.1693651
  • Zhang, J., van Gorp, D. and Kievit, H. (2022). Digital technology and national entrepreneurship: An ecosystem perspective. The Journal of Technology Transfer, 48(3), 1077-1105. https://doi.org/10.1007/s10961-022-09934-0
  • Zhong, R.Y., Xu, X., Klotz, E. and Newman, S.T. (2017). Intelligent manufacturing in the context of industry 4.0: a review. Engineering, 3(5), 616-630. https://doi.org/10.1016/J.ENG.2017.05.015
Toplam 92 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yatay Kesit Analizi, Mikroekonomik Teori, Kalkınma Ekonomisi - Mikro, Uluslararası İktisatta Bölgesel Gelişme ve Küreselleşme, Sürdürülebilir Kalkınma
Bölüm Araştırma Makalesi
Yazarlar

Özlem Fikirli 0000-0002-4003-7276

Hasan Şahin 0000-0001-5922-068X

Gönderilme Tarihi 31 Ekim 2025
Kabul Tarihi 30 Mart 2026
Yayımlanma Tarihi 31 Mart 2026
DOI https://doi.org/10.30784/epfad.1814381
IZ https://izlik.org/JA96HE84UD
Yayımlandığı Sayı Yıl 2026 Cilt: 11 Sayı: 1

Kaynak Göster

APA Fikirli, Ö., & Şahin, H. (2026). How Digitalization Shapes Export Potential: Firm-Level Insights From The EU and Beyond. Ekonomi Politika ve Finans Araştırmaları Dergisi, 11(1), 179-199. https://doi.org/10.30784/epfad.1814381