ChatGPT’nin kullanımının teknoloji kabul modelleri perspektifinden değerlendirilmesi: Youtube videoları üzerine bir araştırma

Year 2025, Issue: 68, 119 - 142, 27.06.2025

İlker Yiğit , Polat Can

https://doi.org/10.26650/CONNECTIST2025-1553102

Abstract

ChatGPT, yenilikçi ve geniş erişime sahip bir yapay zeka sohbet botu olarak, yapay zeka alanında dönüştürücü bir gelişmeyi temsil etmekte ve genel halkın sofistike yapay zeka teknolojisiyle doğrudan etkileşim kurmasına benzersiz bir fırsat sunmaktadır. Bu çalışma, ChatGPT’nin kabulü ve yayılımını etkileyen faktörleri teknoloji kabul modelleri çerçevesinde incelemektedir. Erken benimseyenler ve yenilikçi kullanıcılar tarafından oluşturulan popüler Youtube videoları analiz edilerek, ‘Örtülü Dirichlet Ayrımı’ yöntemi ile ana temalar belirlenmiştir. Bu temalar, performans beklentisi, çaba beklentisi, kolaylaştırıcı koşullar, hazcı motivasyon ve fiyat değeri gibi ‘Birleşik Teknoloji Kabul ve Kullanım Modeli’ çerçevesindeki faktörler açısından değerlendirilmiştir. Algılanan etkileşim, empati ve kişisel yenilik gibi ek unsurlar ise diğer teknoloji kabul modelleri perspektifinden incelenmiştir. Çalışma ayrıca, yapay zeka uygulamalarına giderek daha fazla önem kazanan iş motivasyonu, algılanan özerklik, algılanan yeterlilik ve statü motivasyonu gibi yeni değişkenleri içerecek şekilde mevcut modellerin genişletilmesini önermektedir. Bu bulgular, ChatGPT gibi yapay zeka teknolojilerinin kendine özgü özelliklerini hesaba katacak şekilde teorik modellerin iyileştirilmesine yönelik yollar önermektedir. Bu araştırma, yapay zekanın benimsenmesi ve bunun daha geniş toplumsal etkileri üzerine yapılacak yeni çalışmalara kapı açmaktadır.

Keywords

ChatGPT , konu analizi , teknoloji kabul modelleri , Örtülü Dirichlet Ayrımı , yapay zeka benimsenmesi

Evaluation of the use of ChatGPT from the perspective of technology acceptance models: A research on Youtube videos

Abstract

ChatGPT, an innovative and widely accessible Artifical Intelligence chatbot, represents a transformative advancement in Artificial Intelligence, offering a unique opportunity for the general public to engage directly with sophisticated Artificial Intelligence technology. This study investigates the factors influencing the acceptance and dissemination of ChatGPT by applying established technology acceptance models. By analyzing relevant Youtube videos created by early adopters and innovative users, the study identifies key themes using Latent Dirichlet Allocation. These themes are evaluated within the framework of ‘Unified Theory of Acceptance and Use of Technology,’ focusing on factors such as performance expectancy, effort expectancy, facilitating conditions, hedonic motivation and price value. Additional elements, including perceived interactivity, empathy, and personal novelty, were examined through the lens of other technology acceptance models. The study also proposes extending current models to include new variables that are increasingly relevant to Artificial Intelligence applications, such as work motivation, perceived autonomy, perceived competence, and status motivation. These findings suggest ways to refine theoretical models to account for the unique characteristics of Artificial Intelligence technologies like ChatGPT. This research opens the door for further studies on Artificial Intelligence adoption and its broader societal impact.

Keywords

ChatGPT , topic analysis , technology acceptance models , Latent Dirichlet Allocation , Artificial Intelligence adoption

References

• Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., … Herrera, F. (2023). Explainable AI (XAI): What we know and what is left to attain Trustworthy AI. Information Fusion, 99, 101805. google scholar
• Alomary, A., & Woollard, J. (2015). How is technology accepted by users? A review of technology acceptance models and theories. https:// eprints.soton.ac.uk/382037/ google scholar
• Balakrishnan, J., Abed, S. S., & Jones, P. (2022). The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services? Technological Forecasting and Social Change, 180, 121692. google scholar
• Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022. google scholar
• Boekaerts, M. (1991). Subjective competence, appraisals and self-assessment. Learning and Instruction, 1(1), 1–17. google scholar
• Cabrera-Sánchez, J.-P., Villarejo-Ramos, Á. F., Liébana-Cabanillas, F., & Shaikh, A. A. (2021). Identifying relevant segments of AI applications adopters–expanding the UTAUT2’s variables. Telematics and Informatics, 58, 101529. google scholar
• Chen, G., Fan, J., & Azam, M. (2024). Exploring AI (AI) chatbots adoption among research scholars using unified theory of acceptance and use of technology (UTAUT). Journal of Librarianship and Information Science, 09610006241269189. https://doi.org/10.1177/ 09610006241269189 google scholar
• Colás-Bravo, P., & Quintero-Rodríguez, I. (2023). Youtube as a digital resource for sustainable education. Sustainability, 15(7), 5687. google scholar
• Deci, E. L., & Ryan, R. M. (2000). The ‘what’ and ‘why’ of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01 google scholar
• DiMaggio, P., Nag, M., & Blei, D. (2013). Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of US government arts funding. Poetics, 41(6), 570–606. google scholar
• Duarte, P., & Pinho, J. C. (2019). A mixed methods UTAUT2-based approach to assess mobile health adoption. Journal of Business Research, 102, 140–150. google scholar
• Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2023). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction, 1–20. https://doi.org/10.1080/10447318.2023.2226495 google scholar
• Gatzioufa, P., & Saprikis, V. (2022a). A literature review on users’ behavioral intention toward chatbots’ adoption. Applied Computing and Informatics. https://doi.org/10.1108/ACI-01-2022-0021 google scholar
• Gatzioufa, P., & Saprikis, V. (2022b). A literature review on users’ behavioral intention toward chatbots’ adoption. Applied Computing and Informatics. https://doi.org/10.1108/ACI-01-2022-0021 google scholar
• Gensim: Topic modelling for humans. (2023). https://radimrehurek.com/gensim/intro.html google scholar
• Iis, E. Y., Wahyuddin, W., Thoyib, A., Ilham, R. N., & Sinta, I. (2022). The effect of career development and work environment on employee performance with work motivation as intervening variable at the office of agriculture and livestock in aceh. International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS), 2(2), 227–236. google scholar
• Indurkhya, N., & Damerau, F. J. (2010a). Handbook of natural language processing (Vol. 2). CRC Press. google scholar
• Indurkhya, N., & Damerau, F. J. (2010b). Handbook of natural language processing. Chapman and Hall/CRC. https://www.taylorfrancis. com/books/mono/10.1201/9781420085938/handbook-natural-language-processing-fred-damerau-nitin-indurkhya google scholar
• Kang, H. J., Kim, C., & Kang, K. (2019). Analysis of the trends in biochemical research using Latent Dirichlet Allocation (LDA). Processes, 7(6), 379. google scholar
• Khan, A. N., Jabeen, F., Mehmood, K., Soomro, M. A., & Bresciani, S. (2023). Paving the way for technological innovation through adoption of AI in conservative industries. Journal of Business Research, 165, 114019. google scholar
• Kumar, P., & Lohan, A. (2024). Evaluating ChatGPT adoption through the lens of the technology acceptance model: Perspectives from higher education. International Journal of Technological Learning, Innovation and Development, 15(4), 370–383. https://doi.org/ 10.1504/IJTLID.2024.140316 google scholar
• Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., … Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742 google scholar
• Lee, S., Jones-Jang, S. M., Chung, M., Kim, N., & Choi, J. (2024). Who is using ChatGPT and why? extending the unified theory of acceptance and use of technology (UTAUT) model. Information Research an International Electronic Journal, 29(1), 54–72. google scholar
• 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. google scholar
• Ma, J., Wang, P., Li, B., Wang, T., Pang, X. S., & Wang, D. (2025). Exploring user adoption of ChatGPT: A technology acceptance model perspective. International Journal of Human–Computer Interaction, 41(2), 1431–1445. https://doi.org/10.1080/10447318.2024.2314358 google scholar
• Mankad, S., Han, H. Spring, Goh, J., & Gavirneni, S. (2016). Understanding online hotel reviews through automated text analysis. Service Science, 8(2), 124–138. google scholar
• McCaslin, M. L., & Scott, K. W. (2003). The five-question method for framing a qualitative research study. The Qualitative Report, 8(3), 447–461. google scholar
• Menon, D., & Shilpa, K. (2023). “Chatting with ChatGPT”: Analyzing the factors influencing users’ intention to use the Open AI’s ChatGPT using the UTAUT model. Heliyon, 9(11). https://www.cell.com/heliyon/pdf/S2405-8440(23)08170-7.pdf google scholar
• Mogaji, E., Balakrishnan, J., Nwoba, A. C., & Nguyen, N. P. (2021). Emerging-market consumers’ interactions with banking chatbots. Telematics and Informatics, 65, 101711. google scholar
• OpenAI. (2023). Introducing ChatGPT. https://openai.com/blog/chatgpt google scholar
• Parker, M. A., Valdez, D., Rao, V. K., Eddens, K. S., & Agley, J. (2023). Results and methodological implications of the digital epidemiology of prescription drug references among twitter users: Latent dirichlet allocation (LDA) analyses. Journal of Medical Internet Research, 25, e48405. google scholar
• Praveen, S. V., Ittamalla, R., & Deepak, G. (2021). Analyzing the attitude of Indian citizens towards COVID-19 vaccine–A text analytics study. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 15(2), Article 2 google scholar
• Radziwill, N. M., & Benton, M. C. (2017). Evaluating quality of chatbots and intelligent conversational agents (No. arXiv:1704.04579). arXiv. http://arxiv.org/abs/1704.04579 google scholar
• Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A.-E., & Baneres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in higher education. Computers & Education, 182, 104468. google scholar
• Riyanto, S., Endri, E., & Herlisha, N. (2021). Effect of work motivation and job satisfaction on employee performance: Mediating role of employee engagement. Problems and Perspectives in Management, 19(3), 162. google scholar
• Rodriguez, M. Y., & Storer, H. (2020). A computational social science perspective on qualitative data exploration: Using topic models for the descriptive analysis of social media data*. Journal of Technology in Human Services, 38(1), 54–86. https://doi.org/10.1080/ 15228835.2019.1616350 google scholar
• Rogers, E. M., & Shoemaker, F. F. (1971). Communication of innovations; A aross-cultural Approach. https://eric.ed.gov/?id=ED065999 google scholar
• Rogers, E. M., & Williams, D. (1983). Diffusion of. Innovations Glencoe, IL: The Free Press, 1962). https://ocw.metu.edu.tr/file.php/118/ Week9/rogers-doi-ch5.pdf google scholar
• Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford publications. https://books.google.com/books?hl=tr&lr=&id=Bc_DDAAAQBAJ&oi=fnd&pg=PP1&dq=Self-determination+theory:+Basic+psychological+needs+in+motivation,+development,+and+wellness.&ots=QJfcpadU4g&sig=SWiTuH NlaVspaMwjWP28PBfQwo4 google scholar
• Saari, U. A., Tossavainen, A., Kaipainen, K., & Mäkinen, S. J. (2022). Exploring factors influencing the acceptance of social robots among early adopters and mass market representatives. Robotics and Autonomous Systems, 151, 104033. google scholar
• Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2023.2209881 google scholar
• Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre-and post-adopters. Computers in Human Behavior, 64, 602–620. google scholar
• Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of AI in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368–383. google scholar
• Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2019). Investigating pre-service teachers’ acceptance of Web 2.0 technologies in their future teaching: A Chinese perspective. Interactive Learning Environments, 27(4), 530–546. https://doi.org/10.1080/10494820.2018.1489290 google scholar
• Vaismoradi, M., Jones, J., Turunen, H., & Snelgrove, S. (2016). Theme development in qualitative content analysis and thematic analysis. https://nordopen.nord.no/nord-xmlui/bitstream/handle/11250/2386408/Vaismoradi.pdf google scholar
• Valtolina, S., Matamoros, R. A., Musiu, E., Epifania, F., & Villa, M. (2022). Extended UTAUT model to analyze the acceptance of virtual assistant’s recommendations using interactive visualisations. Proceedings of the 2022 International Conference on Advanced Visual Interfaces, 1–5. https://doi.org/10.1145/3531073.3531129 google scholar
• Van Wingerden, J., & Niks, I. M. (2017). Construction and validation of the perceived opportunity to craft scale. Frontiers in Psychology, 8, 573. google scholar
• 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. google scholar
• 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, 157–178. google scholar
• Wang, Y.-Y., Luse, A., Townsend, A. M., & Mennecke, B. E. (2015). Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems. Information Systems and E-Business Management, 13(4), 769–799. https://doi.org/10.1007/s10257-014-0269-9 google scholar
• Xiong, J., Sun, D., & Wang, Y. (2023). Adoption of AI artifacts: A literature review. Universal Access in the Information Society. https://doi. org/10.1007/s10209-023-00978-z google scholar
• Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., & Li, X. (2011). Comparing twitter and traditional media using topic models. In P. Clough, C. Foley, C. Gurrin, G. J. F. Jones, W. Kraaij, H. Lee, & V. Mudoch (Eds.), Advances in Information Retrieval (Vol. 6611, pp. 338– 349). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_34 google scholar
• Zumstein, D., & Hundertmark, S. (2017). Chatbots–an interactive technology for personalized communication, transactions and services. IADIS International Journal on WWW/Internet, 15(1). https://www.researchgate.net/profile/Darius-Zumstein/publication/322855718_Chatbots_-_An_Interactive_Technology_for_Personalized_Communication_Transactions_and_ Services/links/5a72ecde458515512076b406/Chatbots-An-Interactive-Technology-for-Personalized-Communication-Transactions-and-Services.pdf google scholar