Determinants of Differences in Marketing-Oriented Artificial Intelligence Use Among Businesses in EU Member States: an Empirical Analysis with Eurostat Data

Öz

This study empirically investigates the determinants of cross-country differences in firms’ use of artificial intelligence for marketing and sales across the 27 European Union member states. The Technology–Organization–Environment (TOE) framework is adopted as the theoretical foundation and adapted to the country level. Using harmonized Eurostat ICT enterprise survey data for the period 2021–2024 (particularly isoc_eb_ai and isoc_e_dii), six hypotheses are tested. A two-stage methodology combining k-means clustering and fixed-effects panel regression is employed. The findings indicate that digital infrastructure maturity (measured by the Digital Intensity Index) and ICT specialist intensity are the strongest and most consistent predictors of marketing-oriented AI adoption. Moreover, the North–South/Eastern European divide remains partially significant even after controlling for structural factors. The results also suggest that digital infrastructure affects marketing AI adoption largely indirectly through overall digitalization capacity. The study contributes to the literature by providing one of the pioneering empirical applications of the TOE framework at the country level in a marketing-specific AI context. In addition, it offers a theoretical framing of the “SME paradox,” whereby small firms exhibit adoption rates comparable to large firms in marketing AI usage, and presents supporting empirical evidence. The findings further highlight the critical importance of addressing digital infrastructure investments and human capital development simultaneously for policymakers.

Anahtar Kelimeler

• Artificial Intelligence
• Digital Marketing
• TOE Framework
• EU-27
• Eurostat
• SME Paradox
• Cross-Country Comparative Analysis

AB Üye Ülkelerinde İşletmelerin Pazarlama Amaçlı Yapay Zekâ Kullanımındakı Farklılıkların Belirleyicileri: Eurostat Verileriyle Ampirik Bir Analiz

Öz

Bu çalışma, Avrupa Birliği 27 üye devletinde işletmelerin yapay zekâ teknolojilerini pazarlama ve satış amacıyla kullanımındaki ülkeler arası farklılıkların belirleyicilerini ampirik olarak incelemektedir. Teknoloji–Organizasyon–Çevre (TOE) çerçevesi teorik temel olarak benimsenmiş ve ülke düzeyine uyarlanmıştır. Eurostat’ın 2021–2024 dönemine ait harmonize ICT işletme anketi verileri (özellikle isoc_eb_ai ve isoc_e_dii) kullanılarak altı hipotez test edilmiştir. Çalışmada K-ortalamalar kümeleme analizi ile sabit etkiler panel regresyonunu birleştiren iki aşamalı bir yöntem uygulanmıştır. Bulgular, dijital altyapı olgunluğunun (Dijital Yoğunluk Endeksi ile ölçülen) ve ICT uzman yoğunluğunun pazarlama amaçlı YZ benimsemesinin en güçlü ve tutarlı yordayıcıları olduğunu göstermektedir. Bununla birlikte, Kuzey–Güney/Doğu Avrupa ayrışmasının, kontrol değişkenleri sabit tutulduğunda dahi kısmi anlamlılığını koruduğu tespit edilmiştir. Ayrıca bulgular, dijital altyapının pazarlama YZ benimsemesini büyük ölçüde genel dijitalleşme kapasitesi üzerinden dolaylı olarak etkilediğine işaret etmektedir.Çalışmanın özgün katkısı, TOE çerçevesini ülke düzeyinde ve pazarlama odaklı YZ bağlamında ampirik olarak sınayan öncü modellerden birini sunmasıdır. Bunun yanı sıra çalışma, pazarlama YZ kullanımında küçük işletmelerin büyük işletmelere yakın oranlar sergilemesini ifade eden “KOBİ paradoksu”nu teorik olarak çerçevelemekte ve bu olguya yönelik ampirik bulgular sunmaktadır. Elde edilen sonuçlar, politika yapıcılar açısından dijital altyapı yatırımları ile beşerî sermaye gelişiminin eş zamanlı olarak ele alınmasının kritik önem taşıdığını ortaya koymaktadır.

Anahtar Kelimeler

• Yapay Zekâ
• Dijital Pazarlama
• TOE Çerçevesi
• AB-27
• Eurostat
• KOBİ Paradoksu
• Ülkeler Arası Karşılaştırmalı Analiz

Kaynakça

1. Abou Elgheit, E. (2025). Generative AI as a disruptive innovation: Implications for marketing strategic transformations. Foresight and STI Governance, 19(1), 6–15. https://doi.org/10.17323/fstig.2025.24831
2. Antoniuk, D. ve Kolyada, O. (2024). Adoption of artificial intelligence in European enterprises: A mathematical model based on socioeconomic and technological indicators. Management and Entrepreneurship: Trends of Development, 3(29), 130–139. https://doi.org/10.26661/2522-1566/2024-3/29-12
3. Arroyabe, M. F., Arranz, C. F. A., Fernandez De Arroyabe, I. ve Fernandez de Arroyabe, J. C. (2024). Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment. Technology in Society, 79, 102733. https://doi.org/10.1016/j.techsoc.2024.102733
4. Baron, R. M. ve Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
5. Bick, A., Blandin, A., Deming, D., Fuchs-Schündeln, N. ve Jessen, J. (2026, 9 Nisan). Differences in AI adoption in Europe and the US: Explanations and implications for productivity growth. VoxEU Column, CEPR. https://cepr.org/voxeu/columns/differences-ai-adoption-europe-and-us-explanations-and-implications-productivity
6. Bocean, C. G. (2026). Unraveling the connection between AI adoption and e-commerce performance in the European Union: A cross-country study. Systems, 14(1), 106. https://doi.org/10.3390/systems14010106
7. Bryan, J. D. ve Zuva, T. (2021). A review on TAM and TOE framework progression and how these models integrate. Advances in Science, Technology and Engineering Systems Journal, 6(3), 137–145. https://doi.org/10.25046/aj060316
8. Eurostat (2025a). Digital economy and society statistics — enterprises. Statistics Explained. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Digital_economy_and_society_statistics_-_enterprises (Erişim tarihi: 07.04.2026)

9. Eurostat (2025b). Use of artificial intelligence in enterprises. Statistics Explained. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises (Erişim tarihi: 07.04.2026)
10. Eurostat (2026). The use of artificial intelligence (AI) technologies in the European Union. KS-01-26-009-EN-N. Publications Office of the European Union. https://ec.europa.eu/eurostat/web/products-statistical-reports/w/ks-01-26-009
11. Gualandri, F. ve Kuzior, A. (2024). Drivers of AI adoption in enterprises: A European-wide analysis. Materials Research Proceedings, 45, 240–248. https://doi.org/10.21741/9781644903315-28
12. Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271. https://doi.org/10.2307/1913827
13. Hržica, R., Debelak, K. ve Pevcin, P. (2025). A dual-level model of AI readiness in the public sector: Merging organizational and individual factors using TOE and UTAUT. Systems, 13(8), 705. https://doi.org/10.3390/systems13080705
14. Ionașcu, C. M. (2025). Artificial intelligence adoption in the European Union: A data-driven cluster analysis (2021–2024). Economies, 13(5), 145. https://doi.org/10.3390/economies13050145
15. Kheloufi, S. (2025). Artificial intelligence adoption in European Union enterprises (2024): An empirical analysis of usage patterns by size, sector, technology type, and functional purpose. Journal of Contemporary Business and Economic Studies, 8(2), 80–98.
16. Manta, E.-M., Geambasu, M. C. ve Birlan, I. (2025). Mapping AI adoption across Europe: A cluster analysis of national responsibility. Proceedings of the 19th International Conference on Business Excellence (PICBE), 1532–1545. https://doi.org/10.2478/picbe-2025-0119
17. Mpanza, S. S. (2025). Revisiting the Technological-Organizational-Environmental (TOE) framework and Diffusion of Innovation (DOI): A theoretical review for artificial intelligence (AI) adoption. International Journal of Applied Research in Business and Management, 6(5). https://doi.org/10.51137/wrp.ijarbm.441
18. OECD (2025). AI adoption by small and medium-sized enterprises. OECD Publishing. https://doi.org/10.1787/426399c1-en
19. Pinto, A. S., Abreu, A., Pérez Cota, M. ve Paiva, J. (2025). A meta analysis of TOE factors driving organizational adoption of artificial intelligence across industries. Discover Artificial Intelligence, 5. https://doi.org/10.1007/s44163-025-00747-2
20. Riti, R. I., Bacali, L. ve Abrudan, C. I. (2025). From AI adoption to ESG in industrial B2B marketing: An integrated multi-theory model. Sustainability, 17(19), 8595. https://doi.org/10.3390/su17198595
21. Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312. https://doi.org/10.2307/270723
22. Tornatzky, L. G. ve Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books.
23. Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. MIT Press.