ÜRETKEN YAPAY ZEKÂ UYGULAMALARINDAN ALGILANAN KULLANICI DENEYİMİNİN MEMNUNİYET VE KULLANIMI SÜRDÜRME NİYETİ ÜZERİNDEKİ ETKİLERİ

Öz

Bu araştırma, üretken yapay zekâ uygulamaları bağlamında çok boyutlu bir yapı olarak ele alınan algılanan kullanıcı deneyiminin kullanıcı memnuniyeti ve kullanımı sürdürme niyeti üzerindeki etkilerini incelemektedir. Beklenti–Onay Teorisi ve Teknoloji Kabul Modeli’ni kuramsal temel alan ve literatürdeki mevcut ampirik ve kavramsal çalışmalardan beslenen araştırmada, kullanıcı deneyiminin kullanıcı memnuniyetini pozitif yönde etkilediği; kullanıcı memnuniyetinin ise üretken yapay zekâ teknolojilerinin kullanımını sürdürme niyetinin oluşumunda belirleyici bir rol üstlendiği varsayılan bütüncül bir kavramsal çerçeve önerilmektedir. Ayrıca, kullanıcı memnuniyetinin, kullanıcı deneyimi ile kullanımı sürdürme niyeti arasındaki ilişkide aracı bir etkiye sahip olduğu öngörülmektedir. Araştırmanın evrenini, daha önce üretken yapay zekâ uygulamalarını kullanma deneyimine sahip bireyler oluşturmaktadır. Veriler, çevrim içi anket yöntemi kullanılarak toplam 625 katılımcıdan toplanmıştır. Üretken yapay zekâ uygulamalarını kullanmadığını belirten katılımcılar ile uç değer analizi sonucunda elenen gözlemler veri setinden çıkarılmış ve analizler 548 geçerli gözlem üzerinden gerçekleştirilmiştir. Veri analiz sürecinde IBM SPSS 20 ve SmartPLS 4 programlarından yararlanılmıştır. Ölçüm modelinin geçerlik ve güvenirliği doğrulayıcı faktör analizi ile değerlendirilmiş; araştırma kapsamında geliştirilen hipotezler ise kısmi en küçük kareler yapısal eşitlik modellemesi (PLS-SEM) yöntemiyle test edilmiştir. Elde edilen bulgular, olumlu bir üretken yapay zekâ kullanıcı deneyiminin kullanıcı memnuniyetini güçlü ve anlamlı biçimde artırdığını; kullanıcı memnuniyetinin ise kullanımı sürdürme niyeti üzerinde belirgin ve pozitif bir etkiye sahip olduğunu ortaya koymaktadır. Ayrıca, kullanıcı deneyiminin kullanımı sürdürme niyeti üzerindeki etkisinin, kullanıcı memnuniyeti aracılığıyla dolaylı olarak gerçekleştiği belirlenmiştir. Bu sonuçlar, üretken yapay zekâ uygulamalarının uzun vadeli benimsenmesi ve sürdürülebilir kullanımının sağlanmasında kullanıcı deneyimi ve memnuniyetinin kritik bir öneme sahip olduğunu göstermektedir. Çalışma, üretken yapay zekâ teknolojilerinin kullanıcı odaklı tasarımı ve sürdürülebilir gelişimi bağlamında hem kuramsal hem de uygulamaya yönelik önemli katkılar sunmaktadır.

Anahtar Kelimeler

• Üretken Yapay Zeka Kullanıcı Deneyimi
• Memnuniyet
• Kullanımı Sürdürme Niyeti

THE EFFECTS OF PERCEIVED USER EXPERIENCE WITH GENERATIVE ARTIFICIAL INTELLIGENCE APPLICATIONS ON SATISFACTION AND CONTINUANCE INTENTION

Öz

This study examines the effects of perceived user experience—conceptualized as a multidimensional construct in the context of generative artificial intelligence (AI) applications—on user satisfaction and continuance intention. Grounded in Expectation–Confirmation Theory and the Technology Acceptance Model, and informed by prior empirical and conceptual research, the study proposes a comprehensive conceptual framework in which user experience positively influences user satisfaction, and satisfaction plays a decisive role in shaping users’ intention to continue using generative AI technologies. In addition, user satisfaction is hypothesized to function as a mediating mechanism in the relationship between user experience and continuance intention. The research sample consists of individuals with prior experience using generative AI applications. Data were collected through an online survey administered to 625 participants. Respondents without generative AI usage experience and outliers identified through statistical screening were excluded from the dataset, resulting in 548 valid observations for analysis. Data analysis was conducted using IBM SPSS 20 and SmartPLS 4. The validity and reliability of the measurement model were assessed through confirmatory factor analysis, while the proposed hypotheses were tested using partial least squares structural equation modeling (PLS-SEM). The findings indicate that a favorable generative AI user experience significantly and positively enhances user satisfaction, which, in turn, exerts a strong and positive effect on continuance intention. Furthermore, the effect of user experience on continuance intention is found to occur indirectly through user satisfaction. These results highlight the critical role of user experience and satisfaction in fostering the long-term adoption and sustainable use of generative AI applications. The study offers important theoretical and practical contributions to the user-centered design and sustainable development of generative AI technologies.

Anahtar Kelimeler

• Generative Artificial Intelligence User Experience
• Satisfaction
• Continuance Intention

Kaynakça

1. Alghiffari, A. P., and Matusin, I. O. (2023). Antecedents of Customer Loyalty on AI Chatbot Users İn Banking Applications. Jurnal Pendidikan Tambusai, 7(2), 18915-18927.
2. An, H., Guo, Z., and Shi, Y. (2025). Can Generative AI Features İncrease Users' Continued Use Intention?: The Mediating Role of User Perceived Value and User Satisfaction. In The 2nd Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence.
3. Baig, M. I., and Yadegaridehkordi, E. (2025). Factors İnfluencing Academic Staff Satisfaction And Continuous Usage of Generative Artificial Intelligence (Genaı) in Higher Education. International Journal of Educational Technology in Higher Education, 22(1), 5.
4. Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351–370.
5. Brown, A. (2025). The Impact of Generative AI on Customer Satisfaction in the Tourism Industry. Texas Christian University.
6. Casteleiro-Pitrez, J. (2024). Generative Artificial Intelligence Image Tools Among Designers of the Future: Usability, User Experience and Emotional Analysis. Digital, 4 (2), 316-332.
7. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates.
8. DataReportal (2025). Digital 2026: more than 1 billion people use AI. Access: 30 November 2025, https://datareportal.com/reports/digital-2026-one-billion-people-using-ai

9. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340.
10. Deloitte. (2024). Earning Trust As Gen AI Takes Hold: 2024 Connected Consumer Survey. Access: 20 August 2025, https://www.deloitte.com/us/en/insights/industry/telecommunications/connectivity-mobile-trends-survey.html/
11. DeLone, W. H., and McLean, E. R. (2003). The Delone and Mclean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9–30.
12. Deng, L., Turner, D. E, Gehling, R., and Prince, B. (2010). User experience, satisfaction, and intention for continued use of IT. European Journal of Information Systems, 19 (1), 60-75.
13. Digital Silk (2025). AI Statistics In 2025: Key Trends and Usage Data Access: 20 August 2025, https://www.digitalsilk.com/digital-trends/ai-statistics/
14. Faruk, L. I. D., Pal, D., Funilkul, S., Perumal, T., and Mongkolnam, P. (2025). Introducing CASUX: A Standardized Scale for Measuring the User Experience of Artificial Intelligence Based Conversational Agents. International Journal of Human–Computer Interaction, 41(9), 5274-5298.
15. Fornell, C. and Larcker, D.F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.
16. Fu, Y., Wang, Y., Ye, X., Wu, W., and Wu, J. (2023). Satisfaction and Continuous Usage Intention Towards Mobile Healthcare Services: Transforming User Feedback Into Measurement. Sustainability, 15 (2), 1-21.
17. Goh, F. (2025). Innovate To Elevate!: A Journey Through Mindset and Gen AI to Enhance Customer Experience. World Scientific Publishing.
18. Grand View Research. (2025). Generative AI Market Size, Share & Trends Analysis Report Access: 20 August 2025, https://www.grandviewresearch.com/industry-analysis/generative-ai-market-report/
19. Gupta, R., Nair, K., Mishra, M., Ibrahim, B., and Bhardwaj, S. (2024). Adoption and Impacts of Generative Artificial Intelligence: Theoretical Underpinnings and Research Agenda. International Journal of Information Management Data Insights, 4, 100232.
20. Hair, J. F., Hult, G. T. M., Ringle, C. M. and Sarstedt, M. (2017). Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). (2.Baskı), Sage, Thousand Oaks.
21. Hair, J.F., Tomas, G., Hult, M., Ringle, C.M. and Sarstedt, M. (2014). A Primer on Partial Least Square Structural Equations Modeling (PLS-SEM). Sage.
22. Hassenzahl, M. (2018). The Thing And I: Understanding The Relationship Between User and Product. In Funology 2: From Usability to Enjoyment, (p. 301-313). Cham: Springer International Publishing.
23. Henseler, J., Ringle, C.M. and Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modelling. Journal of the Academy of Marketing Science, 43, 115-135.
24. Hou, A., Yang, Y. X., and Hou, Y. R. (2024). Understanding User Continuance for Generative AI: An Exploration through the Theory of Trying. In 2024 3rd International Conference on Artificial Intelligence and Software Engineering (ICAISE).
25. Hsu, C. L., and Lin, J. C. C. (2023). Understanding The User Satisfaction and Loyalty of Customer Service Chatbots. Journal of Retailing and Consumer Services, 71, 103211.
26. Israfilzade, K., and Vlasenko, D. (2025). Generative AI’s Impact on Customer Satisfaction: A FinTech Perspective. Organizacijų Vadyba: Sisteminiai Tyrimai, 92, 33-49.
27. Jung, Y. M., and Jo, H. (2025). Understanding Continuance Intention of Generative AI in Education: An ECM-Based Study for Sustainable Learning Engagement. Sustainability, 17(13), 6082.
28. Keng, C. J., Sung, P. F., and Chen, Y.-H. (2025). Measuring Artificial Intelligence Customer Experience: Scale Development and Validation. Journal of Retailing and Consumer Services, 76, 1-14.
29. Kim, J. S., Kim, M., and Baek, T. H. (2025). Enhancing User Experience with a Generative AI Chatbot. International Journal of Human-Computer Interaction, 41(1), 651-663.
30. Kim, J. S., and Baek, T. H. (2025). Motivational Determinants of Continuance Usage Intention for Generative AI: An Investment Model Approach for Chatgpt Users in The United States. Behaviour & Information Technology, 44(12), 3080-3096.
31. Kim, T. S., Ignacio, M. J., Yu, S., Jin, H., and Kim, Y. G. (2024). UI/UX for Generative AI: Taxonomy, Trend, and Challenge. IEEE Access, 179891- 179911.
32. Li, J., Cao, H., Lin, L., Hou, Y., Zhu, R., and El Ali, A. (2024). User Experience Design Experts' Perceptions of Generative Artificial Intelligence. In proceedings of the 2024 chı conference on human factors in computing systems: 1-18.
33. Li, Y., Datta, S., Rastegar-Mojarad, M., Lee, K., Paek, H., Glasgow, J., ... and Xu, Y. (2025a). Enhancing Systematic Literature Reviews with Generative Artificial Intelligence: Development, Applications, and Performance Evaluation. Journal of the American Medical Informatics Association, 32(4), 616-625.
34. Li, C., Hao, R., Li, N., and Zhang, C. (2025b). Measuring Customer Experience in AI Contexts: A Scale Development. Journal of Theoretical and Applied Electronic Commerce Research, 20(1), 31.
35. Matharoo, S. (2024). Transforming Retail: Elevating Customer Experience and Efficiency with Generative AI Technique. Asian Journal of Research in Computer Science, 17(12), 179-184.
36. McKinney, V., Yoon, K., and Zahedi, F. M. (2002). The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach. Information Systems Research, 13(3), 296–315.
37. Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460–469.
38. Peruchini, M., da Silva, G. M., and Teixeira, J. M. (2024). Between Artificial Intelligence and Customer Experience: A Literature Review On The İntersection. Discover Artificial Intelligence, 4(1), 4.
39. Sarstedt, M., Ringle, C. M. and Hair, J. F. (2017). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann and A. Vomberg (Eds.), Handbook of market research, (p. 1-40). Springer.
40. Schlagwein, D. and Willcocks, L. (2023). Chatgpt and Others': The Ethics of Using (Productive) Artificial Intelligence in Research and Science. Journal of Information Technology, 38 (3), 232-238.
41. Singla, A., Sukharevsky, A., Yee, L., Chui, M. and Hall, B. (2025, March). The state of AI: How organizations are rewiring to capture value. McKinsey & Company.
42. Statista (2025a). Number of AI tool users worldwide from 2019 to 2024. Access: 18 August 2025, https://www.statista.com/forecasts/1449844/ai-tool-users-worldwide
43. Statista (2025b). Most downloaded generative AI mobile apps worldwide in January 2025, by number of downloads. Access: 18 August 2025, https://www.statista.com/statistics/1554189/top-gen-ai-apps-by-downloads/
44. Swapnil, A., Ge, H., Diagarajan, M. S. and Happonen, A. (2024). Modern State-of-the-Art Generative AI Uses and Practices for Product Innovation, Marketing Strategies, and Enhanced Customer Experience. In 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME).
45. Thorne, S. (2024). Understanding the interplay between Trust, Credibility and Human Factors in the Era of Generative AI. International Journal Of Simulation - Systems, Science and Technology, 25 (1), 1-10.
46. Wang, P., Li, K., Du, Q., and Wang, J. (2024). Customer Experience in AI-Enabled Products: Scale Development and Validation. Journal of Retailing and Consumer Services, 76, 103578.
47. Wolf, M. J., Grodzinsky, F., and Miller, K. W. (2024). Generative AI and Its Implications for Definitions of Trust. Information, 15(9), 1-10.
48. Yee, L., Chui, M., Roberts, R. and Smit, S. (2025). McKinsey Technology Trends Outlook 2025. Access: 18.08.2025, https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-top-trends-in-tech
49. Yıldız, E. (2021). SmartPLS ile Yapısal Eşitlik Modellemesi: Reflektif ve Formatif Yapılar. Seçkin Yayıncılık, Ankara.
50. Zhou, T., and Ma, X. (2025). Examining Generative AI User Continuance Intention Based on The SOR Model. Aslib Journal of Information Management.