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Journals / Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / 2026 / Cilt: 35

EXAMINING CHATGPT USAGE BEHAVIOR WITHIN THE FRAMEWORK OF THE BIG FIVE PERSONALITY TRAITS

BEŞ BÜYÜK KİŞİLİK ÖZELLİĞİ ÇERÇEVESİNDE CHATGPT KULLANIM DAVRANIŞININ İNCELENMESİ

Gülay Güler(ÇAĞ ÜNİVERSİTESİ)0000-0002-2576-7756
Deniz Zeren(ÇUKUROVA ÜNİVERSİTESİ)0000-0003-1197-5375
Pages
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Publication Date
2026-07-21
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results are discussed in terms of their theoretical and practical implications.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Advances in artificial intelligence technologies have rapidly expanded their use across various fields, from individuals' daily lives to their professional careers.
📊
Key Findings & Data:This increase has also intensified academic interest in the behaviors of consumers in Generation Z, who possess the potential to adapt more quickly to new technologies.
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Conclusion & Impact:The results are discussed in terms of their theoretical and practical implications.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:ChatGPT · Kişilik Özellikleri · Yapay Zekâ

Abstract

Advances in artificial intelligence technologies have rapidly expanded their use across various fields, from individuals' daily lives to their professional careers. As individuals increasingly interact with these new technologies every day, their intentions and frequency of using artificial intelligence are on the rise. This increase has also intensified academic interest in the behaviors of consumers in Generation Z, who possess the potential to adapt more quickly to new technologies. Personality traits are one of the most important factors influencing consumer behavior in this regard. In the relevant literature, the effect of consumers' personality traits on AI usage is a research area that attracts the attention of researchers for a comprehensive understanding of the subject. In this study, trends in the usage behavior of ChatGPT, one of the widely known AI applications, are examined within the framework of consumers' personality traits. In this sense, the aim of the study is to explain the intentions and behaviors of Generation Z individuals regarding ChatGPT usage within the framework of the Big Five personality traits. To this end, 127 usable questionnaire forms collected through a face-to-face survey were subjected to Structural Equation Modeling analysis. The research findings show that the personality types of agreeableness, conscientiousness, and openness to experience have a positive and significant effects on the intention to use ChatGPT. No significant relationship was found with the personality types of extraversion and emotional stability and ChatGPT usage intention. The results are discussed in terms of their theoretical and practical implications.

Özet

Yapay zekâ teknolojilerinde yaşanan gelişmeler bireylerin günlük yaşamlarından iş hayatlarına kadar çeşitli alanlarda kullanımını hızla yaygınlaştırmaktadır. Her geçen gün bu yeni teknolojilerle daha fazla etkileşime giren bireylerin yapay zekâ kullanma niyetleri ve sıklıkları artış göstermektedir. Bu artış, özellikle yeni teknolojilere daha hızlı adapte olabilme potansiyeli taşıyan Z kuşağındaki tüketicilerin davranışlarına yönelik akademik ilgiyi de arttırmaktadır. Kişilik özellikleri, bu anlamda tüketici davranışları üzerinde etkili olan en önemli faktörlerden biridir. İlgili alan yazınında tüketicilerin kişilik özelliklerinin yapay zekâ kullanımına etkisi konunun zengin bir anlayışla ele alınabilmesi için araştırmacıların ilgisini çeken bir araştırma alanıdır. Bu çalışmada, yaygın olarak bilinen yapay zekâ uygulamalarından ChatGPT’nin kullanım davranışına yönelik eğilimler, tüketicilerin kişilik özellikleri çerçevesinde incelenmektedir. Bu anlamda çalışmanın amacı, Z kuşağındaki bireylerin beş büyük kişilik özelliği çerçevesinde ChatGPT kullanma niyetlerini ve davranışlarını açıklamaya çalışmaktır. Bu amaçla gerçekleştirilen yüz yüze anket çalışmasıyla toplanan 127 kullanılabilir anket formu Yapısal Eşitlik Modellemesi analizine tabi tutulmuştur. Araştırma bulguları uyumluluk, sorumluluk ve deneyime açıklık kişilik türlerinin ChatGPT kullanma niyeti üzerinde pozitif ve anlamlı bir etki oluşturduğunu göstermektedir. Dışadönüklük ve duygusal denge kişilik türleriyle ise anlamlı bir ilişki saptanamamıştır. Bu bağlamda elde edilen sonuçlar, teori ve uygulama açısından değerlendirilmiştir.

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Cite This Article

Güler, G., Zeren, D. (2026). EXAMINING CHATGPT USAGE BEHAVIOR WITHIN THE FRAMEWORK OF THE BIG FIVE PERSONALITY TRAITS. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt: 35. https://doi.org/10.35379/cusosbil.1696756

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References (76)

  1. Ajiga, D., Okeleke, P., Folorunsho, S., & Ezeigweneme, C. (2024). Enhancing software development practices with AI insights in high-tech companies. Computer Science & IT Research Journal, 5(8), 1897–1919. https://doi.org/10.51594/csitrj.v5i8.1450
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
  3. Al-Sharafi, M. A., Al-Emran, M., Iranmanesh, M., Al-Qaysi, N., Iahad, N. A., & Arpaci, I. (2023). Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interactive Learning Environments, 31(10), 7491–7510. https://doi.org/10.1080/10494820.2022.2075014
  4. Amazon. (2018). Artificial intelligence at Amazon. https://www.amazon.com/b?ie=UTF8&node=16008589011 Erişim: 01.04.2025
  5. Aparicio, M., Bacao, F., & Oliveira, T. (2017). Grit in the path to E-learning success. Computers in Human Behavior, 66, 388–399. https://doi.org/10.1016/j.chb.2016.10.009
  6. Arpaci, I., Al-Emran, M., Al-Qaysi, N., Al-Sharafi, M. A., & Iahad, N. A. (2022). Understanding the social sustainability of the Metaverse by integrating UTAUT2 and big five personality traits: A hybrid SEM-ANN approach. Technology in Society, 71, 102120. https://doi.org/10.1016/j.techsoc.2022.102120
  7. Azeem, S., & Abbas, M. (2025). Personality correlates of academic use of generative artificial intelligence and its outcomes: Does fairness matter? Education and Information Technologies, 1–25. https://doi.org/10.1007/s10639-025-13489-6
  8. Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Technology Studies, 2, 285–309.
  9. Barnett, T., Pearson, A. W., Pearson, R., & Kellermanns, F. W. (2015). Five-factor model personality traits as predictors of perceived and actual usage of technology. European Journal of Information Systems, 24(4), 374–390. https://doi.org/10.1057/ejis.2014.10
  10. Barrick, M. R., & Mount, M. K. (1991). The big five personality dimensions and job performance: A meta‐analysis. Personnel Psychology, 44(1), 1–26. https://doi.org/10.1111/j.1744-6570.1991.tb00688.x
  11. Benet-Martínez, V., & John, O. P. (1998). Los Cinco Grandes across cultures and ethnic groups: Multitrait-multimethod analyses of the Big Five in Spanish and English. Journal of Personality and Social Psychology, 75(3), 729. https://doi.org/10.1037/0022-3514.75.3.729
  12. Bitlisli, Ö., Çetinceli, E., Dinç, M., & Kaygısız, Ü. O. (2013). Beş faktör kişilik özellikleri ile akademik güdülenme ilişkisi: Süleyman Demirel Üniversitesi Isparta Meslek Yüksekokulu öğrencilerine yönelik bir araştırma. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 18(2), 459–480.
  13. Bruck, C. S., & Allen, T. D. (2003). The relationship between big five personality traits, negative affectivity, type A behavior, and work–family conflict. Journal of Vocational Behavior, 63(3), 457–472. https://doi.org/10.1016/S0001-8791(02)00040-4
  14. Cardon, P., Fleischmann, C., Aritz, J., Logemann, M., & Heidewald, J. (2023). The challenges and opportunities of AI-assisted writing: Developing AI literacy for the AI age. Business and Professional Communication Quarterly, 86(3), 257–295. https://doi.org/10.1177/23294906231176517
  15. Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates.
  16. Corr, P., & Matthews, G. (2009). The Cambridge handbook of personality psychology. Cambridge University Press.
  17. Coşkun, F., & Gülleroğlu, H. D. (2021). Yapay zekanın tarih içindeki gelişimi ve eğitimde kullanılması. Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi, 54(3), 947–966. https://doi.org/10.30964/auebfd.916220
  18. Devaraj, S., Easley, R. F., & Crant, J. M. (2008). How does personality matter? Relating the Five-Factor Model to technology acceptance and use. Information Systems Research, 19(1), 93–105. https://doi.org/10.1287/isre.1070.0153
  19. DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology, 93(5), 880–896. https://doi.org/10.1037/0022-3514.93.5.880
  20. Du, C., Tang, M., Wang, C., Zou, B., Xia, Y., & Du, Y. (2025). Who is most likely to accept AI chatbots? A sequential explanatory mixed-methods study of personality and ChatGPT acceptance for language learning. Innovation in Language Learning and Teaching, 1-22. https://doi.org/10.1080/17501229.2025.2555515
  21. Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24, 1709–1734. https://doi.org/10.1007/s10796-021-10186-w
  22. Faruk, L. I. D., Rohan, R., Ninrutsirikun, U., & Pal, D. (2023, December). University students’ acceptance and usage of generative AI (ChatGPT) from a psycho-technical perspective. In Proceedings of the 13th International Conference on Advances in Information Technology (pp. 1–8). https://doi.org/10.1145/3628454.3629552
  23. Forman, N., Udvaros, J., & Avornicului, M. S. (2023). ChatGPT: A new study tool shaping the future for high school students. Future, 5(6), 7. https://doi.org/10.59287/ijanser.562
  24. Friedman, A. (2022, December 6). AI chatbot predicted to replace Google in a couple of years. PhoneArena. https://www.phonearena.com/news/ai-chatbot-could-replace-google-soon_id144120. Erişim: 01.05.2025
  25. Giunta, C. (2017). An emerging awareness of generation Z students for higher education professors. Archives of Business Research, 5(4). https://doi.org/10.14738/abr.54.2962
  26. Gorichanaz, T. (2023). Accused: How students respond to allegations of using ChatGPT on assessments. Learning: Research and Practice, 9(2), 183–196. https://doi.org/10.1080/23735082.2023.2254787
  27. Graziano, W. G., Habashi, M. M., Sheese, B. E., & Tobin, R. M. (2007). Agreeableness, empathy, and helping: A person x situation perspective. Journal of Personality and Social Psychology, 93(4), 583–599. https://doi.org/10.1037/0022-3514.93.4.583
  28. Gupta, M., & Bhaskar, P. (2024). The researcher’s sidekick: Unleashing ChatGPT’s potential in academic research. In Utilizing AI tools in academic research writing (pp. 171–184). IGI Global.
  29. Gündüz, Ş. (2022). Yöneticilerin Z kuşağından beklentileri ve Z kuşağının iş yaşamı beklentileri. Uluslararası Yönetim İktisat ve İşletme Dergisi, 18(3), 733–754. https://doi.org/10.17130/ijmeb.960547
  30. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2022). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (p. 197). Springer Nature.
  31. Handoyo, E., Arfan, M., Soetrisno, Y. A. A., Somantri, M., Sofwan, A., & Sinuraya, E. W. (2018, September). Ticketing chatbot service using serverless NLP technology. In 2018 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) (pp. 325–330). IEEE.
  32. Harb, Y., & Alhayajneh, S. (2019, April). Intention to use BI tools: Integrating technology acceptance model (TAM) and personality trait model. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 494–497). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/JEEIT.2019.8717407
  33. Hu, K. (2023, February 1). ChatGPT sets record for fastest-growing user base. Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/ Erişim: 06.04.2025
  34. Hu, Y., Tan, C. S., Wang, S., Zhang, H., Qian, J., & Wang, Y. (2025). Who is hooked on AI? The role of the Big Five personality traits in compulsive ChatGPT use among Chinese students. The Asia-Pacific Education Researcher, 1–9. https://doi.org/10.1007/s40299-025-01001-0
  35. İrge, N. T. (2022). Çevresel duyarlılığın çalışanların yeşil örgütsel davranışlarına etkisinde kişilik özelliklerinin aracı rolü. İşletme Araştırmaları Dergisi, 14(2), 1293–1315. https://doi.org/10.20491/isarder.2022.1442
  36. Karakose, T., Demirkol, M., Yirci, R., Polat, H., Ozdemir, T. Y., & Tülübaş, T. (2023). A conversation with ChatGPT about digital leadership and technology integration: Comparative analysis based on human–AI collaboration. Administrative Sciences, 13(7), 157. https://doi.org/10.3390/admsci13070157
  37. Kleinman, Z. (2023, February 6). Bard: Google launches ChatGPT rival. BBC News. https://www.bbc.com/news/technology-64546299. Erişim: 08.03.2025
  38. Köroğlu, Y. (2017). Yapay zekâ’nın teorik ve pratik sınırları. Boğaziçi Üniversitesi Yayınları. https://www.cmpe.boun.edu.tr/~yavuz.koroglu/publications/EBES17.pdf. Erişim: 01.05.2025
  39. Lawal, F., et al. (2022). Personality is to a conversational agent what perfume is to a flower. IEEE Consumer Electronics Magazine, 12(6), 20–26. https://doi.org/10.1109/MCE.2022.3180183
  40. Lin, J. C., Younessi, D. N., Kurapati, S. S., Tang, O. Y., & Scott, I. U. (2023). Comparison of GPT-3.5, GPT-4, and human user performance on a practice ophthalmology written examination. Eye, 37(17), 3694–3695. https://doi.org/10.1038/s41433-023-02564-2
  41. Lu, L. C., Chang, W. P., & Chang, H. H. (2014). Consumer attitudes toward blogger’s sponsored recommendations and purchase intention: The effect of sponsorship type, product type, and brand awareness. Computers in Human Behavior, 34, 258–266. https://doi.org/10.1016/j.chb.2014.02.007
  42. Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: How may AI and GPT impact academia and libraries? Library Hi Tech News, 40(3), 26–29. http://dx.doi.org/10.2139/ssrn.4333415
  43. Madran, C., & Akdoğan, T. (2010). Satıcıların kişilikleri ile performanslarının ilişkisi: Beş faktör modeline göre bir analiz. Ç.Ü. Sosyal Bilimler Enstitüsü Dergisi, 19(1), 366–381.
  44. McCarthy, J. (2004). What is artificial intelligence?https://cse.unl.edu/~choueiry/S09-476-876/Documents/whatisai.pdf. Erişim: 02.02.2025
  45. McCrae, R. R., & Costa, P. T. (1987). Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52(1), 81.
  46. McElroy, J. C., Hendrickson, A. R., Townsend, A. M., & DeMarie, S. M. (2007). Dispositional factors in internet use: Personality versus cognitive style. MIS Quarterly, 31(4), 809–820. https://doi.org/10.2307/25148821
  47. McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa… Examine the variables influencing the use of artificial intelligent in-home voice assistants. Computers in Human Behavior, 99, 28–37. https://doi.org/10.1016/j.chb.2019.05.009
  48. Microsoft. (2025). What is artificial intelligence? https://azure.microsoft.com/tr-tr/resources/cloud-computing-dictionary/what-is-artificial-intelligence#s%C3%BCr%C3%BCc%C3%BCs%C3%BCz-ara%C3%A7lar Erişim: 28.04.2025
  49. Miklosik, A., Evans, N., & Qureshi, A. M. A. (2021). The use of chatbots in digital business transformation: A systematic literature review. IEEE Access, 9, 106530–106539. https://doi.org/10.1109/ACCESS.2021.3100885
  50. Miner, A. S., Laranjo, L., & Kocaballi, A. B. (2020). Chatbots in the fight against the COVID-19 pandemic. NPJ Digital Medicine, 3(1), 65.
  51. Minkkinen, M., & Mäntymäki, M. (2023). Discerning between the “easy” and “hard” problems of AI governance. IEEE Transactions on Technology and Society, 4(2), 188–194. https://doi.org/10.1109/TTS.2023.3267382
  52. OpenAI. (2022). ChatGPT: Optimizing language models for dialogue. https://openai.com/chatgpt/overview/ Erişim: 01.04.2025
  53. Özbek, V., Alnıaçık, Ü., Koç, F., Akkılıç, M. E., & Kaş, E. (2014). Kişilik özelliklerinin teknoloji kabulü üzerindeki doğrudan ve dolaylı etkileri: Akıllı telefon teknolojileri üzerine bir araştırma. International Review of Economics and Management, 2(1), 36–57. https://doi.org/10.18825/irem.09956
  54. Pew Research Center. (2020, May 14). On the cusp of adulthood and facing an uncertain future: What we know about Gen Z so far. https://www.pewresearch.org/social-trends/2020/05/14/on-the-cusp-of-adulthood-and-facing-an-uncertain-future-what-we-know-about-gen-z-so-far/
  55. Puiu, S., Demyen, S., Tănase, A. C., Vărzaru, A. A., & Bocean, C. G. (2022). Assessing the adoption of mobile technology for commerce by Generation Z. Electronics, 11(6), 866. https://doi.org/10.3390/electronics11060866
  56. Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated guide and practical guide to statistical analysis. International Journal of Business and Social Science, 1(1), 1–72.
  57. Rice, L., & Markey, P. M. (2009). The role of extraversion and neuroticism in influencing anxiety following computer-mediated interactions. Personality and Individual Differences, 46(1), 35–39. https://doi.org/10.1016/j.paid.2008.08.022
  58. Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
  59. Saif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
  60. Saini, N. (2023, February 7). ChatGPT becomes fastest growing app in the world, records 100mn users in 2 months. Mint. https://www.livemint.com/news/chatgpt-becomes-fastest-growing-app-in-the-world-records-100mn-users-in-2-month-11675484444142.html Erişim: 01.05.2025
  61. Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014.
  62. Schoen, H., & Schumann, S. (2007). Personality traits, partisan attitudes, and voting behavior: Evidence from Germany. Political Psychology, 28(4), 471–498. https://doi.org/10.1111/j.1467-9221.2007.00582.x
  63. Segato, A., Marzullo, A., Calimeri, F., & De Momi, E. (2020). Artificial intelligence for brain diseases: A systematic review. APL Bioengineering, 4(4), 1–36. https://doi.org/10.1063/5.0011697
  64. Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M.,. & Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI - Künstliche Intelligenz, 35(1), 109–118. https://doi.org/10.1007/s13218-020-00689-0
  65. Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling consumers’ adoption intentions of remote mobile payments in the United Kingdom: Extending UTAUT with innovativeness, risk, and trust. Psychology & Marketing, 32(8), 860–873.https://doi.org/10.1002/mar.20823
  66. Stevens, C. D., & Ash, R. A. (2001). Selecting employees for fit: Personality and preferred managerial style. Journal of Managerial Issues 13(4), 500–517.
  67. Svendsen, G. B., Johnsen, J.-A. K., Almås-Sørensen, L., & Vittersø, J. (2013). Personality and technology acceptance: The influence of personality factors on the core constructs of the Technology Acceptance Model. Behaviour & Information Technology, 32(4), 323–334. https://doi.org/10.1080/0144929X.2011.553740
  68. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.
  69. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478. https://doi.org/10.2307/30036540
  70. Völkel, S. T., & Kaya, L. (2021). Examining user preference for agreeableness in chatbots. In Proceedings of the 3rd Conference on Conversational User Interfaces (CUI '21) (Article 38, pp. 1–6). Association for Computing Machinery. https://doi.org/10.1145/3469595.3469633
  71. Wang, W., & Siau, K. (2018). Artificial intelligence: A study on governance, policies, and regulations. In MWAIS 2018 Proceedings (Vol. 40, pp. 1–5).
  72. Wang, Y., Zheng, Y., & Zhu, Y. (2018). How transformational leadership influences employee voice behavior: The roles of psychological capital and organizational identification. Social Behavior and Personality: An International Journal, 46(2), 313–322. https://doi.org/10.2224/sbp.6619
  73. Weng, X., Xia, Q., Ahmad, Z., & Chiu, T. K. (2024). Personality traits for self-regulated learning with generative artificial intelligence: The case of ChatGPT. Computers and Education: Artificial Intelligence, 7, 100315. https://doi.org/10.1016/j.caeai.2024.100315
  74. Winter, J., Dodou, D. & Eisma, Y.B. (2024) Personality and acceptance as predictors of ChatGPT use. Discover Psychology, 4(57), 1-16. https://doi.org/10.1007/s44202-024-00161-2
  75. Yıldıran, Y. D., & Erdem, Ş. (2024). Yapay zeka tabanlı chatbot hizmetinin kullanıcı alışkanlık ve davranışları üzerine etkileri ve bir uygulama. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 46(1). https://doi.org/10.14780/muiibd.1381666
  76. Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M.,. & Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI - Künstliche Intelligenz, 35(1), 109–118. https://doi.org/10.1007/s13218-020-00689-0