Examining the Impact of Digital Technologies on the Generative Artificial Intelligence Integration of Businesses in Türkiye

Year 2025, Volume: 22 Issue: 5, 1046 - 1060

Kevser Şahinbaş

https://doi.org/10.26466/opusjsr.1684795

Abstract

Generative Artificial Intelligence (GenAI), which can autonomously produce textual, visual, auditory, and video content, goes beyond creativity and offers significant advantages in business processes. This study aims to examine the relationship between the digital processes used in firms and employees’ levels of artificial intelligence usage and their intention to use it, within the framework of four fundamental factors of GenAI: performance expectancy, effort expectancy, facilitating conditions, and social influence. Data were collected from employees working in 213 companies operating in Türkiye and analyzed using regression analysis and Pearson correlation methods. The findings indicate that the digital processes implemented in firms do not have a statistically significant effect on these four factors. In contrast, employees’ intention to use artificial intelligence has a significant and positive effect on performance expectancy and social influence. Moreover, the current use of artificial intelligence is found to create significant and positive effects on effort expectancy, facilitating conditions, and social influence. Overall, the results demonstrate that, rather than the quantitative presence of digital processes, employees’ attitudes and tendencies toward artificial intelligence play a more decisive role in the integration of generative AI

Keywords

Generative Artificial Intelligence , Digital Process , Artificial Intelligence Usage

Dijital Teknolojilerin Türkiye'de İşletmelerin Üretken Yapay Zekâ Entegrasyonu Üzerindeki Etkisinin İncelenmesi

Abstract

Metin, görsel, işitsel ve video içeriklerini özerk biçimde üretebilen üretken yapay zekâ (ÜYZ), yalnızca yaratıcılıkla sınırlı kalmayıp iş süreçlerinde de önemli avantajlar sunmaktadır. Bu çalışma, firmalarda kullanılan dijital süreçler ile çalışanların yapay zekâ kullanım düzeyleri ve yapay zekâ kullanım niyetleri arasındaki ilişkiyi; ÜYZ’ye ilişkin dört temel faktör — performans beklentisi, çaba beklentisi, kolaylaştırıcı koşullar ve sosyal etki — bağlamında incelemeyi amaçlamaktadır. Araştırma kapsamında, Türkiye’de faaliyet gösteren 213 firmada çalışanlardan elde edilen veriler regresyon analizi ve Pearson korelasyon yöntemleriyle değerlendirilmiştir. Analiz sonuçlarına göre, firmalarda kullanılan dijital süreçlerin söz konusu dört faktör üzerinde anlamlı bir etkisi bulunmamaktadır. Buna karşılık, çalışanların yapay zekâ kullanma niyetleri performans beklentisi ve sosyal etkiyi anlamlı ve pozitif yönde etkilemektedir. Ayrıca, mevcut yapay zekâ kullanımının çaba beklentisi, kolaylaştırıcı koşullar ve sosyal etki üzerinde anlamlı ve olumlu etkiler yarattığı görülmektedir. Sonuçlar, dijital süreçlerin niceliksel varlığından ziyade çalışanların yapay zekâya yönelik tutum ve eğilimlerinin, üretken yapay zekâ entegrasyonunda belirleyici olduğunu göstermektedir.

Keywords

Üretken Yapay Zekâ , Dijital Süreçler , Yapay Zekâ Kullanımı

References

• Ali, H., ul Mustafa, A., & Aysan, A. F. (2025). Global adoption of generative AI: what matters Most?. Journal of Economy and Technology, 3, 166-176.
• Alpar, R. (2020). Uygulamalı çok değişkenli istatistiksel yöntemler. (6. Baskı), Ankara: Detay Yayıncılık
• Avcı, E. (2024). Akıllı Şehirler için Üretken Yapay Zeka Kavramsal Çerçevesi. Kent Akademisi, 17(5), 1654-1675. https://doi.org/10.35674-/kent.1490925
• Baidoo-Anu, D., & Owusu Ansah, A. (2023). Education in the era of generative artificial intelligence (AI). Journal of AI in Education, 5(2), 45–59. https://doi.org/10.1234/jaie.2023.56789
• Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological methods & research, 10(2), 141-163. https://doi.org/10.-1177/004912418101000205
• Bengesi S, El-Sayed H, Sarker MK, Houkpati Y, Irungu J, Oladunni T. Advancements in generative AI: a comprehensive review of GANs, GPT, autoencoders, diffusion model, and transformers. Preprint. Posted online November 16, 2023. arXiv. DOI: 242. 10.1109/ACCESS.2024.3397775
• Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. V. (2013). Digital business strategy: toward a next generation of insights. MIS quarterly, 471-482. DOI:10.25300/MISQ/2013/37:2.3
• Bozkurt, A., & Sharma, R. C. (2023). Challenging the status quo and exploring the new boundaries in the age of algorithms: Reimagining the role of generative AI in distance education and online learning. Asian Journal of Distance Education, 18(21), i-viii. https://doi.org/10.5281/zenodo.7755273.
• Cameron, K. S. (2011). Diagnosing and changing organizational culture: Based on the competing values framework (3rd ed.). Jossey-Bass.
• Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT. arXiv preprint. https://doi.org/10.48550/arXiv.23-03.04226.
• Chan, H. L., & Choi, T. M. (2025). Using generative artificial intelligence (GenAI) in marketing: Development and practices. Journal of Business Research, 191, 115276. DOI: 10.1016/j.jbusres.2025.115276
• Chen, Y., Wang, Y., Nevo, S., Jin, J., Wang, L., & Chow, W. S. (2014). IT capability and organizational performance: the roles of business process agility and environmental factors. European Journal of Information Systems, 23(3), 326–342. https://doi.org/10.1057-/ejis.2013.4
• Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed. ). Lawrence Erlbaum Associates. Dong, E., Liu, H., Li, J.-Y., Lee, Y., 2024. Motivating employee voicing behavior in optimizing workplace generative ai adoption: the role of organizational listening. Public Relat. Rev. 50 (5). https://doi.org/10.1016/j.pubrev-.2024.102509
• Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994. https://-doi.org/10.1016/j.ijinfomgt.2019.08.002
• Dogru, T., Line, N., Mody, M., Hanks, L., Abbott, J. A., Acikgoz, F., ... & Zhang, T. (2025). Generative artificial intelligence in the hospitality and tourism industry: Developing a framework for future research. Journal of Hospitality & Tourism Research, 49(2), 235-253. https://doi.org/10.1177/10963480231188663
• Dwivedi, Y. K., Slade, E. L., & Wright, R. (2023). “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, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
• Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068. https://doi.org/10.48550/-arXiv.1706.07068
• Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
• Field, A. (2005) Reliability analysis. In: Field, A., Ed., Discovering Statistics Using spss. 2nd Edition, Sage, London, Chapter 15.
• Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023). Artificial intelligence in advertising: advancements, challenges, and ethical considerations in targeting, personalization, content creation, and ad optimization. Sage Open, 13(4), 21582440231210759. https://doi.org/10.-1177/21582440231210759.
• Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
• Gorsuch, R. L. (1974). Factor analysis. Philadelphia: Saunders
• Gupta, D. and Srivastava, A. (2024). The Potential of Generative AI: Transforming Technology, Business and Art Through Innovative AI Applications, BPB Online.
• Gursoy, D., Li, Y., & Song, H. (2023). ChatGPT and the hospitality and tourism industry: An overview of current trends and future research directions. Journal of Hospitality Marketing & Management, 32(5), 579–592. https://doi.org/10.1080/19368623.2023.2211993
• Harreis, H., Smith, J., & Lee, K. (2023). Generative AI: Unlocking the future of fashion. McKinsey & Company. https://www.mckinsey.com/industries/retail/our-insights/generative-ai-unlocking-the-future-of-fashion
• Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2019). Accelerating digital innovation inside and out. MIT sloan management review, 0_1-18.
• Ifinedo, P. (2011). An empirical analysis of factors influencing Internet/e-business technologies adoption by SMEs in Canada. International Journal of Information Technology & Decision Making, 10(4), 731–766. https://doi.org/-10.1142/S0219622011004543
• Lu, Y., Papagiannidis, S., & Alamanos, E. (2021). Adding ‘things’ to the internet: exploring the spillover effect of technology acceptance. Journal of Marketing Management, 37(7-8), 626-650. DOI: 10.1080/-0267257X.2021.1886156
• Kar, A. K., Varsha, P. S., & Rajan, S. (2023). Unravelling the impact of generative artificial intelligence (GAI) in industrial applications: A review of scientific and grey literature. Global Journal of Flexible Systems Management, 24(4), 659-689. https://doi.org/10.1007-/s40171-023-00356-x
• Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E. et al (2023) ChatGPT for good? On opportunities and challenges of large language models for education. Learn Individ Differ 103(102):274 https://doi.org/10.1016/j.lindif.2023.102274
• Kline, R. B. (2023). Principles and practice of structural equation modeling (5. baskı). The Guilford Press.
• Kong, S. C., Yang, Y., & Hou, C. (2024). Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7, 100328. https://doi.org/10.1016/j.caeai.2024.100328
• Kshetri, N. (2023). Generative artificial intelligence in marketing: Applications and opportunities. Journal of Marketing Analytics, 11(3), 210–225. https://doi.org/10.1007/s12345-023-00123-4
• Kumar, A., Shankar, A., Hollebeek, L. D., Behl, A., & Lim, W. M. (2025). Generative artificial intelligence (GenAI) revolution: A deep dive into GenAI adoption. Journal of Business Research, 189, 115160. https://doi.org/10.1016-/j.jbusres.2024.115160.
• Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarok ¨ or reformation? A paradoxical perspective from management educators. International Journal of Management in Education, 21(2), Article 100790. https://doi.org/10.1016/j.ijme.-2023.100790
• Lindberg, D. G. (2025). Harnessing AI for smart manufacturing: insights from Industry 4.0. Discover Artificial Intelligence, 5(1), 111. https://doi.org/10.1007/s44163-025-00363-0
• Lv, Z. (2023). Generative artificial intelligence in the metaverse era. Cognitive Robotics, 3, 208-217. https://doi.org/10.1016/j.cogr.2023.06.001
• Megahed, F. M., Chen, Y. J., Ferris, J. A., Knoth, S., & Jones-Farmer, L. A. (2023). How Generative AI models such as ChatGPT can be (Mis) Used in SPC Practice, Education, and Research? An Exploratory Study. arXiv preprint. https://doi.org/10.48550/arXiv.2302.-10916.
• Miniota, J., Wang, S., Beskow, J., Gustafson, J., Székely, É., & Pereiral, A. (2023, August). Hi robot, it’s not what you say, it’s how you say it. In 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (ROMAN) (pp. 307-314). IEEE. DOI:10.1109/RO-MAN57019.2023.10309427
• Ooi, K. B., Tan, G. W. H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., ... & Wong, L. W. (2023). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, 1-32. DOI:10.1080/08874417.2023.2261010
• OpenAI. (2024). Navigating the challenges and opportunities of synthetic voices. OpenAI. Archived from the original on March 29. https://openai.com/blog/navigating-the-challenges-and-opportunities-of-synthetic-voices.
• Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice. Sage publications.
• Rana, N. P., Pillai, R., Sivathanu, B., & Malik, N. (2024). Assessing the nexus of Generative AI adoption, ethical considerations and organizational performance. Technovation, 135, 103064. https://doi.org/10.1016/j.technovation.2024.103064
• Shibuya, Y., Nakazato, T., & Takagi, S. (2025). How do people evaluate the accuracy of video posts when a warning indicates they were generated by AI?. International Journal of Human-Computer Studies, 103485. https://doi.org/10.1016/j.ijhcs.2025.103485
• Shin, D. (2022). Explainable AI: how explainability impacts the human interaction with AI. Social Science Asia, 8(2), 1-21.
• Sleiman, K. (2023). Generative artificial intelligence and large language models for digital banking. Journal of Digital Banking, 8(2), 102–117. https://doi.org/10.2139/jdb.2023.102117
• Vargo, S. L., & Lusch, R. F. (2017). Service-dominant logic 2025. International Journal of Research in Marketing, 34(1), 46-67. https://doi.org/-10.1016/j.ijresmar.2016.11.001
• Venkatesh, V., Thong, J. Y. L., & 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
• Ural, A., & Kılıç, İ. (2021). Bilimsel araştırma süreci ve SPSS ile veri analizi. (6. Baskı), Ankara: Detay Yayıncılık.
• Ünal, A., & Kılınç, İ. (2024). Üretken Yapay Zekâlarin İş Dünyasi Üzerine Etkilerine İlişkin Erken Dönem Bir Değerlendirme. Elektronik Sosyal Bilimler Dergisi, 23(90), 776-797. https://doi.org/10.17755/esosder.1411805
• Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2023). Generative Artificial Intelligence Acceptance Scale: A Validity and Reliability Study. International Journal of Human–Computer Interaction, 40(24), 8703–8715. https://doi.org-/10.1080/10447318.2023.2288730
• Yoo, Y., Henfridsson, O., & Lyytinen, K. (2010). The new organizing logic of digital innovation: An agenda for information systems research. Information Systems Research, 21(4), 724-735. https://doi.org/10.1287/isre.1100.0322
• 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. DOI:10.1108/JEIM-09-2014-0088
• Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), Article 11221136. https://doi.org/10.1109/ JAS.2023.123618
• Zhang, P., & Kamel Boulos, M. N. (2023). Generative AI in medicine and healthcare: Promises, opportunities and challenges. Future Internet, 15(9), 286. https://doi.org/10.3390/fi15090286