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Üniversite öğrencilerinin yapay zekâ (yz) kullanım davranışlarını etkileyen faktörlerin yapısal bir model ile araştırılması

Yıl 2025, Cilt: 15 Sayı: 2, 825 - 838, 27.06.2025

Erkan Arı

https://doi.org/10.30783/nevsosbilen.1615736

Öz

Yapay zeka (YZ) teknolojilerinin hızlı bir şekilde günlük yaşamın her alanına entegre edilmesi, bireylerin bu teknolojilere yönelik kullanım davranışlarını etkileyen bir dizi faktörün ortaya çıkmasına neden olmuştur. Birçok tüketici, günlük yaşamlarında YZ tabanlı uygulamaları, cihazları ve hizmetleri benimsemekte ve kullanmaktadır. Ancak, üniversite öğrencilerinin YZ uygulamalarını kullanma konusundaki tüketici davranışlarını inceleyen araştırmalar oldukça sınırlıdır. Bu çalışmada, YZ kullanımını etkileyen temel faktörler; alışkanlık, kolaylık, davranışsal niyet, kullanım davranışı, teknoloji korkusu ve tüketici güveni bağlamında ele alınacaktır. Literatürdeki mevcut teorik yaklaşımlar ve ampirik bulgular ışığında, bu faktörlerin YZ kullanımına olan etkileri analiz edilmiş ve yapay zeka teknolojilerine yönelik tutumların anlaşılmasına katkı sağlamıştır. Çalışmada literatürden yararlanılarak oluşturulan anket Kütahya Dumlupınar İktisadi ve İdari Bilimler Fakültesi’nde eğitim gören 320 öğrenciye uygulanmış ve veri derleme aracının iç tutarlılık katsayısı Cronbach Alfa (CA) değeri 0,95 olarak hesaplanmıştır. Veriler Google form aracılığıyla çevrimiçi olarak toplanmıştır. Önerilen araştırma modeli ve ilişkileri kısmi en küçük kareler yapısal eşitlik modellemesi (PLS-SEM) kullanılarak test edilmiştir. Analiz sonuçları, YZ ilişkin alışkanlık ve kolaylığın kullanım niyetini arttırdığı belirlenmiştir.

Anahtar Kelimeler

Yapay Zeka (YZ) Alışkanlık Kolaylık

Kaynakça

  • Al-Gahtani, S.S., Hubona, G.S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: culture and the acceptance and use of IT. Information and Management., 44 (8), 681–691. https://doi.org/10.1016/j.im.2007.09.002.
  • Ajzen, I. (1991). The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50 (2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T.
  • Başar E.E., & Kes Erkul A. (2024). Factors Affecting the Attitude of Medical Doctors in Türkiye towards Using Artificial Intelligence Applications in Healthcare Services. Bezmialem Science., 12(3):297-308.
  • Cabrera-S´anchez, J-P., Villarejo-Ramos, A.F., Li´ebana-Cabanillas, F., & Shaikh, A.A. (2021). Identifying relevant segments of AI applications adopters – Expanding the UTAUT2’s variables. Telematics and Informatics, 58, 1-15. https://doi.org/10.1016/j.tele.2020.101529
  • Chauhan, Sumedha, & Jaiswal, Mahadeo (2016). Determinants of acceptance of ERP software training in business schools: empirical investigation using UTAUT model. International Journal of Management Education, 14 (3), 248–262. https://doi.org/10.1016/j.ijme.2016.05.005.
  • Cohen, J. (1992). A power primer. Psychological Bulletin. 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155
  • Dağlı, İ. (2022). Yapay Zekâ Teknolojilerinde Etkili Faktörler Üzerine Bir Model Denemesi: En Başarılı Ülkelerle Panel Veri Analizi. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 17(2), 368 – 386. Doi: 10.17153/oguiibf.1039958.
  • Davis, F. (1985). A Technology Acceptance Model for Empirically Testing New End-User Information Systems. Massachusetts Institute of Technology, (December 1985), 291.
  • Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319–340.
  • Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data—Evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
  • Ecevit, M. (2022). Müşteri İlişkileri Yönetimi ve Yapay Zeka. Kitap Bölümü, Kriter Yayınları,-81.
  • Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/ 002224378101800104
  • Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Philosophy & Rhetoric (Vol. 10). Reading, Mass., Addison-Wesley.
  • García, A.S., Fern´andez-Sotos, P., Fern´andez-Caballero, A., Navarro, E., Latorre, J.M., Rodriguez-Jimenez, R., Gonz´alez, P. (2019). Acceptance and use of a multi-modal avatar-based tool for remediation of social cognition deficits. Journal of Ambient Intelligence and Humanized Computing, 1–12. DOI: 10.1007/s12652-019-01418-8
  • Gharaibeh, M.K., & Arshad, M.R.M. (2018). Determinants of intention to use mobile banking in the North of Jordan: extending UTAUT2 with mass media and trust. Journal of Engineering and Applied Science, 13 (8), 2023–2033. http://dx.doi.org/10.3923/jeasci.2018.2023.2033
  • Hair, J., Hult, T., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks. CA: Sage Publications. Inc.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror. mirror on the wall: a comparative evaluation of compositebased structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632. https://doi.org/10.1007/ s11747-017-0517-x
  • Henseler, J., Ringle. C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  • Kamran, H. (2024). Pazarlamada Yapay Zekanın Kullanımı: Yapay Zeka Pazarlama Arçalarının Tüketici Kabülüne İlişkin Bir Araştırma. Yüksek Lisans Tezi, Bursa Uludağ Üniversitesi, Sosyal Bilimler Enstitüsü İşletme Anabilimdalı.
  • Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62, 15-25.https://doi.org/10.1016/j.bushor.2018.08.004
  • Kim, D., Park, J., Morrison, A.M., Management, R., Sciences, C., & Sciences, F. (2008). A model of traveller acceptance of mobile technology. International Journal of Tourism Research, 10 (5), 393–407. https://doi.org/10.1002/jtr.669
  • Khasawneh, O.Y. (2018). Technophobia without boarders: The influence of technophobia and emotional intelligence on technology acceptance and the moderating influence of organizational climate. Computers in Human Behavior, 88, 210–218. DOI: 10.1016/j.chb.2018.07.007
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature 521 (7553), 436–444. DOI: 10.1038/nature14539
  • Martínez-C´orcoles, M., Teichmann, M., & Murdvee, M. (2017). Assessing technophobia and technophilia: development and validation of a questionnaire. Technology in Society, 51, 183–188. https://doi.org/10.1016/j.techsoc.2017.09.007
  • Martins, C., Oliveira, T., & Popoviˇc, A. (2014). Understanding the internet banking adoption: a unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34 (1), 1–13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002
  • Merhi, M., Hone, K., & Tarhini, A. (2019). A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust. Technology in Society, 59, 101151. https://doi.org/10.1016/j.techsoc.2019.101151
  • Özer, S., Yazıcı, A.S., Akgül, S., & Yıldırım, A. (2023). Okullarda yapay zekâ kullanımına ilişkin öğretmen görüşleri. Ulusal Eğitim Dergisi,3(10), 1776-1794.
  • Ramírez-Correa, P., Rond´an-Catalu˜na, F.J., Arenas-Gait´an, J., & Martín-Velicia, F. (2019). Analysing the acceptation of online games in mobile devices: an application of UTAUT2. Journal of Retailing and Consumer Service, 50, 85–93. DOI: 10.1016/j.jretconser.2019.04.018
  • Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 1–13. https://doi.org/10.1016/j.chbr.2020.100014
  • Sharma, V.M., & Klein, A. (2020). Consumer perceived value, involvement, trust, susceptibility to interpersonal influence, and intention to participate in online group buying. Journal of Retailing and Consumer Service, 52, 1–11. DOI: 10.1016/j.jretconser.2019.101946
  • Shaw, N., & Sergueeva, K. (2019). The non-monetary benefits of mobile commerce: extending UTAUT2 with perceived value. International Journal of Information Management, 45, 44–55. https://doi.org/10.1016/j.ijinfomgt.2018.10.024
  • Sivarajah, U., Kamal, M.M., Irani, Z., & Weerakkody, V. (2016). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001

Investigation of factors affecting artificial intelligence (AI) usage behaviours of university students with a structural model

Yıl 2025, Cilt: 15 Sayı: 2, 825 - 838, 27.06.2025

Erkan Arı

https://doi.org/10.30783/nevsosbilen.1615736

Öz

The rapid integration of artificial intelligence (AI) technologies into all areas of daily life has led to the emergence of a number of factors that affect individuals' usage behaviour towards these technologies. Many consumers adopt and use AI-based applications, devices and services in their daily lives. However, research examining the consumer behaviour of university students in using AI applications is quite limited. In this study, the main factors affecting AI usage will be discussed in the context of habit, convenience, behavioural intention, usage behaviour, fear of technology and consumer trust. In the light of existing theoretical approaches and empirical findings in the literature, the effects of these factors on AI usage are analysed and contribute to the understanding of attitudes towards AI technologies. In the study, the questionnaire created by utilising the literature was applied to 320 students studying at Kütahya Dumlupınar Faculty of Economics and Administrative Sciences and the internal consistency coefficient Cronbach's Alpha (CA) value of the data collection tool was calculated as 0.95. Data were collected online via Google form. The proposed research model and its relationships were tested using partial least squares structural equation modelling (PLS-SEM). The results of the analyses show that habit and convenience regarding AI increase the intention to use.

Anahtar Kelimeler

Artifical Intelligence (AI) Habit Convenience

Kaynakça

  • Al-Gahtani, S.S., Hubona, G.S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: culture and the acceptance and use of IT. Information and Management., 44 (8), 681–691. https://doi.org/10.1016/j.im.2007.09.002.
  • Ajzen, I. (1991). The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50 (2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T.
  • Başar E.E., & Kes Erkul A. (2024). Factors Affecting the Attitude of Medical Doctors in Türkiye towards Using Artificial Intelligence Applications in Healthcare Services. Bezmialem Science., 12(3):297-308.
  • Cabrera-S´anchez, J-P., Villarejo-Ramos, A.F., Li´ebana-Cabanillas, F., & Shaikh, A.A. (2021). Identifying relevant segments of AI applications adopters – Expanding the UTAUT2’s variables. Telematics and Informatics, 58, 1-15. https://doi.org/10.1016/j.tele.2020.101529
  • Chauhan, Sumedha, & Jaiswal, Mahadeo (2016). Determinants of acceptance of ERP software training in business schools: empirical investigation using UTAUT model. International Journal of Management Education, 14 (3), 248–262. https://doi.org/10.1016/j.ijme.2016.05.005.
  • Cohen, J. (1992). A power primer. Psychological Bulletin. 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155
  • Dağlı, İ. (2022). Yapay Zekâ Teknolojilerinde Etkili Faktörler Üzerine Bir Model Denemesi: En Başarılı Ülkelerle Panel Veri Analizi. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 17(2), 368 – 386. Doi: 10.17153/oguiibf.1039958.
  • Davis, F. (1985). A Technology Acceptance Model for Empirically Testing New End-User Information Systems. Massachusetts Institute of Technology, (December 1985), 291.
  • Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319–340.
  • Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data—Evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
  • Ecevit, M. (2022). Müşteri İlişkileri Yönetimi ve Yapay Zeka. Kitap Bölümü, Kriter Yayınları,-81.
  • Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/ 002224378101800104
  • Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Philosophy & Rhetoric (Vol. 10). Reading, Mass., Addison-Wesley.
  • García, A.S., Fern´andez-Sotos, P., Fern´andez-Caballero, A., Navarro, E., Latorre, J.M., Rodriguez-Jimenez, R., Gonz´alez, P. (2019). Acceptance and use of a multi-modal avatar-based tool for remediation of social cognition deficits. Journal of Ambient Intelligence and Humanized Computing, 1–12. DOI: 10.1007/s12652-019-01418-8
  • Gharaibeh, M.K., & Arshad, M.R.M. (2018). Determinants of intention to use mobile banking in the North of Jordan: extending UTAUT2 with mass media and trust. Journal of Engineering and Applied Science, 13 (8), 2023–2033. http://dx.doi.org/10.3923/jeasci.2018.2023.2033
  • Hair, J., Hult, T., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks. CA: Sage Publications. Inc.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror. mirror on the wall: a comparative evaluation of compositebased structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632. https://doi.org/10.1007/ s11747-017-0517-x
  • Henseler, J., Ringle. C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  • Kamran, H. (2024). Pazarlamada Yapay Zekanın Kullanımı: Yapay Zeka Pazarlama Arçalarının Tüketici Kabülüne İlişkin Bir Araştırma. Yüksek Lisans Tezi, Bursa Uludağ Üniversitesi, Sosyal Bilimler Enstitüsü İşletme Anabilimdalı.
  • Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62, 15-25.https://doi.org/10.1016/j.bushor.2018.08.004
  • Kim, D., Park, J., Morrison, A.M., Management, R., Sciences, C., & Sciences, F. (2008). A model of traveller acceptance of mobile technology. International Journal of Tourism Research, 10 (5), 393–407. https://doi.org/10.1002/jtr.669
  • Khasawneh, O.Y. (2018). Technophobia without boarders: The influence of technophobia and emotional intelligence on technology acceptance and the moderating influence of organizational climate. Computers in Human Behavior, 88, 210–218. DOI: 10.1016/j.chb.2018.07.007
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature 521 (7553), 436–444. DOI: 10.1038/nature14539
  • Martínez-C´orcoles, M., Teichmann, M., & Murdvee, M. (2017). Assessing technophobia and technophilia: development and validation of a questionnaire. Technology in Society, 51, 183–188. https://doi.org/10.1016/j.techsoc.2017.09.007
  • Martins, C., Oliveira, T., & Popoviˇc, A. (2014). Understanding the internet banking adoption: a unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34 (1), 1–13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002
  • Merhi, M., Hone, K., & Tarhini, A. (2019). A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust. Technology in Society, 59, 101151. https://doi.org/10.1016/j.techsoc.2019.101151
  • Özer, S., Yazıcı, A.S., Akgül, S., & Yıldırım, A. (2023). Okullarda yapay zekâ kullanımına ilişkin öğretmen görüşleri. Ulusal Eğitim Dergisi,3(10), 1776-1794.
  • Ramírez-Correa, P., Rond´an-Catalu˜na, F.J., Arenas-Gait´an, J., & Martín-Velicia, F. (2019). Analysing the acceptation of online games in mobile devices: an application of UTAUT2. Journal of Retailing and Consumer Service, 50, 85–93. DOI: 10.1016/j.jretconser.2019.04.018
  • Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 1–13. https://doi.org/10.1016/j.chbr.2020.100014
  • Sharma, V.M., & Klein, A. (2020). Consumer perceived value, involvement, trust, susceptibility to interpersonal influence, and intention to participate in online group buying. Journal of Retailing and Consumer Service, 52, 1–11. DOI: 10.1016/j.jretconser.2019.101946
  • Shaw, N., & Sergueeva, K. (2019). The non-monetary benefits of mobile commerce: extending UTAUT2 with perceived value. International Journal of Information Management, 45, 44–55. https://doi.org/10.1016/j.ijinfomgt.2018.10.024
  • Sivarajah, U., Kamal, M.M., Irani, Z., & Weerakkody, V. (2016). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ekonometrik ve İstatistiksel Yöntemler
Bölüm İKTİSAT
Yazarlar

Erkan Arı 0000-0001-6012-0619

Erken Görünüm Tarihi 25 Haziran 2025
Yayımlanma Tarihi 27 Haziran 2025
Gönderilme Tarihi 8 Ocak 2025
Kabul Tarihi 10 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 15 Sayı: 2

Kaynak Göster

APA Arı, E. (2025). Üniversite öğrencilerinin yapay zekâ (yz) kullanım davranışlarını etkileyen faktörlerin yapısal bir model ile araştırılması. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 15(2), 825-838. https://doi.org/10.30783/nevsosbilen.1615736
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