Üretken Yapay Zekâ Kabul Modeli Temelinde KOBİ’ler Üzerine Bir Araştırma

Yıl 2025, Cilt: 20 Sayı: 2, 1 - 16, 29.12.2025

Fevziye Bekar

https://izlik.org/JA52LJ59LD

Öz

Bu araştırmanın amacı KOBİ’ler tarafından üretken yapay zekânın benimsenme eğilimlerini incelemektir. Bu kapsam da teknoloji kabul modeli temelinde yeni geliştirilmiş olan Üretken yapay zekâ kabul modeli temel alınmıştır. Küçük ve orta boy işletmesi olan girişimcilerin yapay zekâ kullanımlarını etkileyen unsurlar incelenmektedir. Yapay zekâya dair girişimcilerin performans beklentisi, çaba beklentisi, sosyal etkinin davranışsal niyet üzerindeki etkisi incelenirken; yapay zekâya dair kolaylaştırıcı koşulların gerçek davranış üzerindeki etkisi de incelenmektedir. Araştırma kapsamında nicel araştırma yöntemi tercih edilmiştir. Kolayda örneklem yöntemi kullanılmış olup 557 girişimciden veri elde edilmiştir. Elde edilen veriler AMOS v.23 programı, yapısal eşitlik modellemesi ile analiz edildi. Araştırma sonucunda performans beklentisi ve sosyal etkinin davranışsal niyet üzerinde pozitif ve anlamlı bir etkisi olduğu sonucuna ulaşılırken; çaba beklentisinin davranışsal niyet üzerinde bir etkisinin olmadı; kolaylaştırıcı koşulların da gerçek davranış üzerinde bir etkisi olmadığı sonucuna ulaşılmıştır.

Anahtar Kelimeler

Üretken Yapay Zeka Kabul Modeli , Teknoloji Kabul Modeli , KOBİ , Girişimcilik

A Study on SMEs Based on the Generative Artificial Intelligence Acceptance Model

https://izlik.org/JA52LJ59LD

Öz

The purpose of this research is to examine the adoption trends of generative artificial intelligence by small and medium enterprises. In this context, the newly developed generative artificial intelligence acceptance model was taken as basis based on the technology acceptance model. The factors affecting the use of artificial intelligence by entrepreneurs with small and medium-sized enterprises are examined. While the effect of performance expectancy, effort expectancy, social influence on behavioural intention of entrepreneurs regarding artificial intelligence is examined; the effect of facilitating conditions regarding artificial intelligence on actual behaviour is also examined. Quantitative research method was preferred within the scope of the research. Convenience sampling method was used and data obtained from 557 entrepreneurs. The obtained data were analyzed with AMOS v.23 program, structural equation modeling. As a result of the research, it was concluded that performance expectation and social influence had a positive and significant effect on behavioral intention; effort expectation had no effect on behavioral intention; and facilitating conditions had no effect on actual behavior.

Anahtar Kelimeler

Generative Artificial Intelligence Acceptance Model , Technology Acceptance Model , SME , Entrepreneurship

Etik Beyan

Ethics Approval: Permission for the survey used as a data collection tool in this study was obtained from the Gümüşhane University Rectorate Scientific Research and Publication Ethics Committee with the decision numbered E-95674917-108.99-253432 in its meeting dated 30/05/2025 and numbered 2024/5.

Destekleyen Kurum

Gümüşhane Üniversitesi

Kaynakça

• Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
• Atlı, H. F. (2024). Digital Transformation and Success Factors of SMEs in Agriculture & Food Marketing with New Technologies (Artificial Intelligence, Blockchain, Machine Learning and Internet of Things). Journal of Human and Social Sciences Research, 13(3), 1192-1218.
• Baş, T. (2008). Anket nasıl hazırlanır nasıl uygulanır nasıl değerlendirilir. Ankara: Seçkin Yayıncılık.
• Bayram, R., Epli, B., Tahtabiçen, B., & Karaman, S. (2025). KOBİ Yönetim Süreçlerinde Yapay Zekâ Entegrasyonu ve SAAS Tabanlı Uygulamaların Potansiyeli. İşletme Araştırmaları Dergisi, 17(1), 662-675. https://doi.org/10.20491/isarder.2025.1995
• Börekci, C., & Çelik, Ö. (2024). Exploring The Role of Digital Literacy in University Students' Engagement with AI through the Technology Acceptance Model. Sakarya University Journal of Education, 14(2), (Special Issue: Artificial Intelligence Tools and Education), 228-249.
• Bozkurt, A. (2023). ChatGPT, üretken yapay zeka ve algoritmik paradigma değişikliği. Alanyazın, 4(1), 63-72.
• Bran, F., Bodislav, D. A., Călin, A. M., & Mănescu, A. M. (2025). Empowering SMEs through generative AI: Opportunities, challenges, and strategic implications for sustainable innovation. European Journal of Sustainable Development, 14(4), 27. https://doi.org/10.14207/ejsd.2025.v14n4p27
• Çam, H. (2012). Türkiye’deki üniversitelerde bulut bilişim teknolojisinin uygulanabilirliğinin teknoloji kabul modeli yaklaşımıyla belirlenmesi (Yayımlanmamış Doktora Tezi). Atatürk Üniversitesi Sosyal Bilimleri Enstitüsü, Erzurum.
• Camridge Dictionary (2024). Artificial Intelligence. Retrieved March 30, 2025, from https://dictionary.cambridge.org/dictionary/english/artificial-intelligence
• Cheng, C. C., Wei, C. C., Chu, T. J., & Lin, H. H. (2022). AI predicted product portfolio for profit maximisation. Applied Artificial Intelligence, 36(1), 2083799.1-21.
• Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
• Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral dissertation, Massachusetts Institute of Technology. Boston.
• Doğan, M., Rana, Ş. E. N., and Yılmaz, V. (2015). İnternet bankacılığına ilişkin davranışların planlanmış davranış teorisi ve teknoloji kabul modeli kullanılarak önerilen bir yapısal eşitlik modeliyle incelenmesi. Uşak Üniversitesi Sosyal Bilimler Dergisi, 8(2), 1-22.
• Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). Opinion Paper:“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International journal of information management, 71, 1-63, https://doi.org/10.1016/j.ijinfomgt.2023.102642%E2%80%9D
• Gansser, O. A., & Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 1-15. 101535.
• Ghimire, A., & Edwards, J. (2024). Generative AI Adoption in Classroom in Context of Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT). arXiv preprint arXiv:2406.15360. 1-6.
• Gupta, S., Saha, R., Kaur, J., Kathuria, S., & Paul, J. (2021). Factors impacting innovation performance for entrepreneurs in India. International Journal of Entrepreneurial Behavior & Research, 27(2), 356-377.
• Ilyas, M., ud din, A., Haleem, M., & Ahmad, I. (2023). Digital entrepreneurial acceptance: an examination of technology acceptance model and do-it-yourself behaviour. Journal of Innovation and Entrepreneurship, 12(1), 15. 1-19.
• Karaoğlan Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2024). Generative artificial intelligence acceptance scale: A validity and reliability study. International Journal of Human-Computer Interaction, 40(24), 8703-8715.
• Üner Kaya, A., & Bekar, F. (2024). The Use of Artificial Intelligence in Entrepreneurship. In Empowering Entrepreneurial Mindsets with AI (pp. 33-56). IGI Global.
• Kim, Y., Blázquez, V., & Oh, T. (2024). Determinants of generative AI system adoption and usage behavior in Korean companies: Applying the UTAUT model. Behavioral Sciences, 14(11), 1035. https://doi.org/10.3390/bs14111035
• Li, K. (2023). Determinants of college students' actual use of AI-based systems: An extension of the technology acceptance model. Sustainability, 15(6), 1-16. 5221.
• Mays, W. (1952). Can machines think?. Philosophy, 27(101), 148-162.
• McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). "A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955". AI Magazine, 27(4), 12-14.
• McMullen, J. S., & Shepherd, D. A. (2006). Entrepreneurial action and the role of uncertainty in the theory of the entrepreneur. Academy of Management review, 31(1), 132-152.
• Mecek, G. (2020). International definition criteria and conceptualisation of small and medium-sized enterprises (SMEs). Journal of Economics Business Politics and International Relations, 6(1), 29-55.
• Na, S., Heo, S., Choi, W., Kim, C., & Whang, S. W. (2023 ). Artificial intelligence (AI)-based technology adoption in the construction industry: a cross national perspective using the technology acceptance model. Buildings, 13(10), 2518. 1-23.
• OECD. (2024). Generative AI and the SME workforce. Paris, France: OECD Publishing. Retrieved March 30, 2025, from https://www.oecd.org/publications/generative-ai-and-the-sme-workforce_2d08b99d-en.html
• Otto, D., Assenmacher, V., Bente, A., Gellner, C., Waage, M., Deckert, R., ... & Kuche, J. (2024). student acceptance of AI-based feedback systems: an analysis based on the technology acceptance model (TAM). In INTED2024 Proceedings. 3695-3701. IATED.
• Oxford English Dictionary (2024). Artificial Intelligence. Retrieved March 30, 2025, from https://www.oed.com/dictionary/artificial-intelligence_n?tl=true
• Özsungur, F. (2019). The effects of technology acceptance and use behaviour on women's entrepreneurship motivation factors. Asia Pacific Journal of Innovation and Entrepreneurship, 13(3), 367-380.
• Rajaram, K., & Tinguely, P. N. (2024). Generative artificial intelligence in small and medium enterprises: Navigating its promises and challenges. Business Horizons, 67(5), 629-648.
• Sánchez, E., Calderón, R., & Herrera, F. (2025). Artificial intelligence adoption in SMEs: Survey based on TOE–DOI framework, primary methodology and challenges. Applied Sciences, 15(12), 6465. https://doi.org/10.3390/app15126465
• Şenyuva, Z. (2024). Girişimci Olma Kararının Arkasındaki Faktörler Arası Etkileşimin DEMATEL Yöntemi ile Analizi. İş ve İnsan Dergisi, 11(1), 77-91.
• Sofiyah, F. R., Dilham, A., Hutagalung, A. Q., Yulinda, Y., Lubis, A. S., & Marpaung, J. L. (2024). The chatbot artificial intelligence as the alternative customer services strategic to improve the customer relationship management in real-time responses. International Journal of Economics and Business Research, 27(5), 45-58.
• STATISTA (2024). Artificial Intelligence - Turkey. 25.03.2025. Retrieved March 25, 2025, from https://www.statista.com/outlook/tmo/artificial-intelligence/turkey.
• Su, Y., & Li, M. (2021). Applying technology acceptance model in online entrepreneurship education for new entrepreneurs. Frontiers in Psychology, 12, 1-11. 713239.
• Tran, H., & Murphy, P. J. (2023). Generative artificial intelligence and entrepreneurial performance. Journal of Small Business and Enterprise Development, 30(5), 853-856.
• TUIK (2024a). Küçük ve Orta Boy Girişim İstatistikleri Bilgi Talebi. 25.03.2025. Retrieved from https://data.tuik.gov.tr/Bulten/Index?p=Kucuk-ve-Orta-Buyuklukteki-Girisim-Istatistikleri-2023-53543
• TUIK (2024b). Girişimcilerde bilişim teknolojileri kullanım araştırması. 29.03.2025. Retrieved from https://data.tuik.gov.tr/Bulten/Index?p=Girisimlerde-Bilisim-Teknolojileri-Kullanim-Arastirmasi-2024-53446
• Türk Dil Kurumu Dictionary (2024). Artificial Intelligence. Retrieved March 30, 2025, from https://sozluk.gov.tr/
• Upadhyay , N., Upadhyay, S., & Dwivedi, Y. K. (2022). Theorising artificial intelligence acceptance and digital entrepreneurship model. International Journal of Entrepreneurial Behaviour & Research, 28(5), 1138-1166.
• Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
• Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412.
• Wang, C., Ahmad, S. F., Ayassrah, A. Y. B. A., Awwad, E. M., Irshad, M., Ali, Y. A., ... & Han, H. (2023). An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon, 9(8). 1-20
• Williams , M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of enterprise information management, 28(3), 443-488.
• Yang, S., & Appleget, C. (2024 ). An exploration of preservice teachers' perceptions of generative AI: Applying the technological acceptance model. Journal of Digital Learning in Teacher Education, 40(3), 159-172.
• Zaremohzzabieh, Z., Abu Samah, B., Muhammad, M., Omar, S. Z., Bolong, J., Hassan, M. S., & Mohamed Shaffril, H. A. (2015). A test of the technology acceptance model for understanding the ICT adoption behaviour of rural young entrepreneurs. International Journal of Business and Management, 10(2), 158-169.
• Zhai , Y., Zhang, L., & Yu, M. (2024). AI in human resource management: Literature review and research implications. Journal of the Knowledge Economy, 1-37.