Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective

Yıl 2026, Cilt: 34 Sayı: 2 , 290 - 310 , 30.04.2026

Mehmet Fikret Gelibolu

https://doi.org/10.24106/kefdergi.1939356

https://izlik.org/JA96NA47ED

Öz

This study investigates the factors influencing higher education students’ self-reported use of generative AI (Gen-AI) chatbots through an extended Technology Acceptance Model (TAM). The model incorporates trust and individual impact alongside perceived usefulness and perceived ease of use to better explain students’ adoption behavior. Usage is defined as the self-reported frequency of chatbot use rather than post-adoption continuance intention. A quantitative, cross-sectional survey was conducted with 303 higher education students. Data were analyzed using Structural Equation Modeling (SEM) in Smart-PLS after confirming the reliability and validity of the measurement model. It's shown that perceived ease-of-use significantly affects both perceived usefulness and self-reported usage. Perceived usefulness also positively influences usage frequency. Trust shapes students’ perceptions of ease of use and usefulness but does not directly affect usage. Moreover, usage has a strong positive impact on individual outcomes, indicating academic and personal benefits associated with frequent use. Ease of use and perceived usefulness are the key drivers of students’ Gen-AI use. Trust influences adoption indirectly by shaping these perceptions. Sustained use of these tools enhances academic and personal outcomes, and the extended TAM proves to be a suitable framework for explaining Gen-AI adoption in higher education contexts.

Anahtar Kelimeler

Generative artificial intelligence (Gen-AI) , Technology acceptance model (TAM) , Trust , Individual impact , Higher education

Yüksek Öğretim Öğrencilerinin Üretken Yapay Zekâ Sohbet Robotlarını Kullanımının Belirleyicileri: Genişletilmiş Teknoloji Kabul Modeli (TKM) Bakış Açısı

https://izlik.org/JA96NA47ED

Öz

Bu çalışma, genişletilmiş bir Teknoloji Kabul Modeli (TKM) aracılığıyla, yükseköğretim öğrencilerinin üretken yapay zekâ (Gen-AI) sohbet robotlarını kendi bildirimlerine göre kullanmalarını etkileyen etmenleri araştırmaktadır. Model, öğrencilerin benimseme davranışlarını daha iyi açıklamak için algılanan yarar ve algılanan kullanım kolaylığının yanı sıra güven ve bireysel etkiyi de içermektedir. Kullanım, benimseme sonrası devam etme niyeti yerine, sohbet robotu kullanımının kendi bildirimlerine göre sıklığı olarak tanımlanmıştır. 303 yükseköğretim öğrencisiyle nicel, kesitsel bir anket çalışması yapılmıştır. Veriler, ölçüm modelinin güvenilirliği ve geçerliliği doğrulandıktan sonra Smart-PLS'de Yapısal Eşitlik Modellemesi (YEM) kullanılarak analiz edilmiştir. Algılanan kullanım kolaylığının hem algılanan yararı hem de gerçek kullanımı önemli ölçüde etkilediği gösterilmiştir. Algılanan yarar ayrıca kullanım sıklığını da olumlu yönde etkilemektedir. Güven, öğrencilerin kullanım kolaylığı ve yarar algılarını şekillendirmekte ancak kullanımı doğrudan etkilememektedir. Üstelik kullanımın bireysel sonuçlar üzerinde güçlü bir olumlu etkisi vardır ve sık kullanımla ilişkili akademik ve kişisel yararı ortaya koymaktadır. Kullanım kolaylığı ve algılanan yarar, öğrencilerin üretken yapay zekâyı kullanmalarının temel belirleyicileridir. Güven, bu algıları şekillendirerek dolaylı olarak benimsemeyi etkilemektedir. Bu araçların sürekli kullanımı akademik ve kişisel sonuçları iyileştirmekte ve genişletilmiş TKM, yükseköğretim bağlamlarında üretken yapay zekânın benimsenmesini açıklamak için uygun bir çerçeve olduğunu kanıtlamaktadır.

Anahtar Kelimeler

Üretken yapay zekâ , Teknoloji kabul modeli (TKM) , Güven , Bireysel etki , Yüksek öğretim

Kaynakça

• Agyare, B., Asare, J., Kraishan, A., Nkrumah, I., & Adjekum, D. K. (2025). A cross-national assessment of artificial intelligence (AI) Chatbot user perceptions in collegiate physics education. Computers and Education: Artificial Intelligence, 8, Article 100365. https://doi.org/10.1016/j.caeai.2025.100365
• Al-Kadi, A., & Ali, J. K. M. (2024). A holistic approach to ChatGPT, Gemini, and Copilot in english learning and teaching. Language Teaching Research Quarterly, 43, 155-166. https://doi.org/10.32038/ltrq.2024.43.09
• Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon, 10(11). https://doi.org/10.1016/j.heliyon.2024.e31887
• Ashraf, H., Qureshi, A., & Mahmood, A. (2025). Effects of ChatGPT on students’ academic performance in Pakistan higher education classrooms. Scientific Reports, 15, Article 16434. https://doi.org/10.1038/s41598-025-92625-1
• Bagozzi, R.P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of Academy of Marketing Science, 16, 74–94. https://doi.org/10.1007/BF02723327
• Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500
• Beldad, A. D., & Hegner, S. M. (2018). Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: A structural equation modeling approach. International Journal of Human–Computer Interaction, 34(9), 882–893. https://doi.org/10.1080/10447318.2017.1403220
• Boubker, O. (2024). From chatting to self-educating: Can AI tools boost student learning outcomes? Expert Systems with Applications, 238, Article 121820. https://doi.org/10.1016/j.eswa.2023.121820
• Butt, S., Mahmood, A., Saleem, S., Rashid, T., & Ikram, A. (2021). Students’ performance in online learning environment: The role of task technology fit and actual usage of system during COVID-19. Frontiers in Psychology, 12, Article 759227. https://doi.org/10.3389/fpsyg.2021.759227
• Büyükeke, A. (2025). Student acceptance of generative AI tools in programming courses: Findings from Türkiye through an extended technology acceptance model. Journal of University Research, 8(4), 543-556. https://doi.org/10.32329/uad.1728785
• Castillo-Martínez I.M., Flores-Bueno D., Gómez-Puente S.M., & Vite-León V. O. (2024). AI in higher education: a systematic literature review. Frontiers in Education. 9, Article 1391485. https://doi.org/10.3389/feduc.2024.1391485
• Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8
• Chen, X., Jiang, L., Zhou, Z., & Li, D. (2025). Impact of perceived ease of use and perceived usefulness of humanoid robots on students' intention to use. Acta Psychologica, 258, Article 105217. https://doi.org/10.1016/j.actpsy.2025.105217
• Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727-1739. https://doi.org/10.1080/10447318.2022.2050543
• Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on adoption and use of ChatGPT: A survey analysis. Journal of Medical Internet Research, 25, Article e47184. https://doi.org/10.2196/47184
• Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273–290. https://doi.org/10.1016/j.compedu.2017.12.001
• Dahri, N. A., Yahaya, N., & Al-Rahmi, W. M. (2025). Exploring the influence of ChatGPT on student academic success and career readiness. Education and information technologies, 30(7), 8877-8921. https://doi.org/10.1007/s10639-024-13148-2
• Dai, Y., Lai, S., Lim, C. P., & Liu, A. (2023). ChatGPT and its impact on research supervision: Insights from Australian postgraduate research students. Australasian Journal of Educational Technology, 39(4), 74-88. https://doi.org/10.14742/ajet.8843
• Davis, F. D. (1987). User acceptance of information systems: The technology acceptance model (TAM) (Working paper). University of Michigan, School of Business Administration, Division of Research.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
• DeLone, W. H., & 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. https://doi.org/10.1080/07421222.2003.11045748
• Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & 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
• 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
• Gelibolu, M. (2024). Tüketicilerin yapay zekâ temelli sesli asistanları kullanma niyetine etki eden faktörler: Algılanan güvenin aracılık rolü. [Factors affecting consumers’ intention to use artificial intelligence-based voice assistants: mediating role of perceived trust. Tüketici ve Tüketim Araştırmaları Dergisi, 16(2). https://doi.org/10.15659/ttad.16.2.183
• Gelibolu, M., & Mouloudj, K. (2025). Motivators and demotivators of consumers’ smart voice assistant usage for online shopping. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), Article 152. https://doi.org/10.3390/jtaer20030152
• Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660.https://doi.org/10.5465/annals.2018.0057
• Gökçearslan, Ş., Yıldız Durak, H., & Esiyok, E. (2023). Emotion regulation, e‐learning readiness, technology usage status, in‐class smartphone cyberloafing, and smartphone addiction in the time of COVID‐19 pandemic. Journal of Computer Assisted Learning, 39(5), 1450-1464. https://doi.org/10.1111/jcal.12785
• Göküş, Ş., & Yılmaz, Z. (2025). İlahiyat lisans öğrencilerinin din eğitiminde chatbot kullanmaya yönelik davranışsal niyetlerinin farklı değişkenler açısından incelenmesi [Investigation of theology undergraduate students' behavioral intention to use chatbot in religious education in terms of different variables]. Dinbilimleri Akademik Araştırma Dergisi, 25(2), 1003-1043. https://doi.org/10.33415/daad.1688342
• Gupta, S., Arora, A., Singh, S., & Jain, J. (2025). AI feel millennials: Prioritizing the intentions towards adoption of AI-enabled chatbots using fuzzy-AHP approach. Journal of Science and Technology Policy Management. Advance online publication. https://doi.org/10.1108/JSTPM-09-2023-0159
• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer. https://doi.org/10.1007/978-3-030-80519-7
• Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced issues in partial least squares structural equation modeling (PLS-SEM). Sage. https://doi.org/10.3926/oss.37
• Heilala, V., Araya, R., & Hämäläinen, R. (2025, March). Beyond text-to-text: An overview of multimodal and generative artificial intelligence for education using topic modeling. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (pp. 54-63). https://doi.org/10.1145/3672608.3707764
• Henseler, J., Ringle, C.M., & 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. https://doi.org/10.1007/s11747-014-0403-8
• Holzmann, P., Gregori, P., & Schwarz, E. J. (2025). Students’ little helper: Investigating continuous-use determinants of generative AI and ethical judgment. Education and Information Technologies, 30(17), 24991-25011. https://doi.org/10.1007/s10639-025-13708-0
• Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53-60. https://academic-publishing.org/index.php/ejbrm/article/view/1224/1187
• Iancu, I., & Iancu, B. (2023). Interacting with chatbots later in life: A technology acceptance perspective in COVID-19 pandemic situation. Frontiers in Psychology, 13, Article 1111003. https://doi.org/10.3389/fpsyg.2022.1111003
• Ibrahim, A., & Shiring, E. (2022). The relationship between educators' attitudes, perceived usefulness, and perceived ease of use of instructional and web-based technologies: implications from technology acceptance model (TAM). International Journal of Technology in Education, 5(4), 535-551. https://doi.org/10.46328/ijte.285
• Iranmanesh, M., Annamalai, N., Kumar, K. M., & Foroughi, B. (2022). Explaining student loyalty towards using WhatsApp in higher education: an extension of the IS success model. The Electronic Library, 40(3), 196-220. https://doi.org/10.1108/EL-08-2021-0161
• Karahan Adalı, G. & Bilgili, A. (2025). Generative AI in higher education: Students’ perspectives on adoption, ethical concerns, and academic impact. Acta Infologica, 9(1), 147-166. https://doi.org/10.26650/acin.1670197
• Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
• Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
• 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, Article 100328. https://doi.org/10.1016/j.caeai.2024.100328
• Kumar, P., Anu, & Parmod. (2025). Exploring the impact of ChatGPT usability on attitudes and beliefs among Indian higher education students: a structural analysis. International Journal of Knowledge and Learning, 18(3), 319-330. https://doi.org/10.1504/IJKL.2025.145989
• Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human factors, 46(1), 50-80. https://doi.org/10.1518/hfes.46.1.50_3039
• Lefever, S., Dal, M., & Matthíasdóttir, Á. (2007). Online data collection in academic research: advantages and limitations. British journal of educational technology, 38(4), 574-582. https://doi.org/10.1111/j.1467-8535.2006.00638.x
• Liu, X., Liu, Y., Dai, Y., & Fu, J. (2026). Academic stress and university students’ dependency on generative artificial intelligence: A multiple mediation model using PLS-SEM. BMC Psychology, 14, Article 216. https://doi.org/10.1186/s40359-026-03986-9
• Malik, R., Shrama, A., Trivedi, S., & Mishra, R. (2021). Adoption of Chatbots for learning among university students: Role of perceived convenience and enhanced performance. International Journal of Emerging Technologies in Learning, 16(18), 200–212. https://doi.org/10.3991/ijet.v16i18.24315
• Memon, M. Q., Lu, Y., Memon, A. R., Memon, A., Munshi, P., & Shah, S. F. A. (2022). Does the impact of technology sustain students’ satisfaction, academic and functional performance: An analysis via interactive and self-regulated learning? Sustainability, 14(12), Article 7226. https://doi.org/10.3390/su14127226
• Mirriahi, N., Marrone, R., Barthakur, A., Gabriel, F., Colton, J., Yeung, T. N., ... & Kovanovic, V. (2025). The relationship between students’ self-regulated learning skills and technology acceptance of GenAI. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.10006
• Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies in classrooms: Five strategies, including prompts. The Wharton School Research Paper. https://dx.doi.org/10.2139/ssrn.4391243
• Mustofa, R. H., Kuncoro, T. G., Atmono, D., & Hermawan, H. D. (2025). Extending the technology acceptance model: The role of subjective norms, ethics, and trust in AI tool adoption among students. Computers and Education: Artificial Intelligence, 8, Article 100379. https://doi.org/10.1016/j.caeai.2025.100379
• Ofosu-Ampong, K., Acheampong, B., Kevor, M. O., & Amankwah-Sarfo, F. (2023). Acceptance of artificial intelligence (ChatGPT) in education: Trust, innovativeness and psychological need of students. Innovativeness and Psychological Need of Students. https://dx.doi.org/10.2139/ssrn.5255328
• Özgül, E. (2026). A systematic review of studies on the use of generative artificial intelligence tools in programming education. Kastamonu Education Journal, 34(1), 172-185. https://doi.org/10.24106/kefdergi.1878122
• Parsonage, G., Horton, M., & Read, J. (2023). Trust acceptance mapping-designing intelligent systems for use in an educational context. In International Conference on Human-Computer Interaction (pp. 34-50). Springer. https://doi.org/10.1007/978-3-031-34735-1_3
• Pitts, G., & Motamedi, S. (2025). Understanding human-AI trust in education. Telematics and Informatics Reports, 100270. https://doi.org/10.1016/j.teler.2025.100270
• Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
• Sakib, M. N., Islam, M., Fahlevi, M., Rahman, M. S., Younus, M., & Rahman, M. M. (2025). Factors influencing users' intention to adopt ChatGPT based on the extended technology acceptance model. Computers in Human Behavior: Artificial Humans, Article 100204. https://doi.org/10.1016/j.chbah.2025.100204
• Senali, M. G., Iranmanesh, M., Ghobakhloo, M., Foroughi, B., Asadi, S., & Rejeb, A. (2024). Determinants of trust and purchase intention in social commerce: Perceived price fairness and trust disposition as moderators. Electronic Commerce Research and Applications, 64, Article 101370. https://doi.org/10.1016/j.elerap.2024.101370
• Shahzad, M. F., Xu, S., & Asif, M. (2025). Factors affecting generative artificial intelligence, such as ChatGPT, use in higher education: An application of technology acceptance model. British Educational Research Journal, 51(2), 489-513. https://doi.org/10.1002/berj.4084
• Sharma, V., Jangir, K., Gupta, M., & Rupeika-Apoga, R. (2024). Does service quality matter in FinTech payment services? An integrated SERVQUAL and TAM approach. International Journal of Information Management Data Insights, 4(2), Article 100252. https://doi.org/10.1016/j.jjimei.2024.100252
• Shin, D. (2020). How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance. Computers in Human Behavior, 109, Article 106344. https://doi.org/10.1016/j.chb.2020.106344
• Shin, D. (2021). Embodying algorithms, enactive artificial intelligence, and the extended cognition: You can see as much as you know about algorithm. Journal of Information Science, 1–14. https://doi.org/10.1177/0165551520985495
• Sio, C. I., Mann, A., Fan, L., Cheung, A., & Lee, L. H. (2025). Perceptions of AI-CBT: trust and barriers in Chinese postgrads. arXiv preprint arXiv:2602.03852. https://doi.org/10.48550/arXiv.2602.03852
• Sledgianowski, D., & Kulviwat, S. (2009). Using social networking sites: The effects of playfulness, critical mass and trust in a hedonic context. The Journal of Computer Information Systems, 49(4), 74–83. https://doi.org/10.1080/08874417.2009.11645342
• Stöhr, C., Ou, A. W., & Malmström, H. (2024). Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence, 7, Article 100259. https://doi.org/10.1016/j.caeai.2024.100259
• Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355-366. https://doi.org/10.1177/20965311231168423
• Suh, B., & Han, I. (2002). Effect of trust on customer acceptance of Internet banking. Electronic Commerce Research and Applications, 1(3–4), 247–263. https://doi.org/10.1016/S1567-4223(02)00017-0
• Tian, W., Ge, J., Zhao, Y., & Zheng, X. (2024). AI Chatbots in Chinese higher education: adoption, perception, and influence among graduate students—an integrated analysis utilizing UTAUT and ECM models. Frontiers in Psychology, 15, Article 1268549. https://doi.org/10.3389/fpsyg.2024.1268549
• Vartiainen, H., & Tedre, M. (2023). Using artificial intelligence in craft education: crafting with text-to-image generative models. Digital Creativity, 34(1), 1-21. https://doi.org/10.1080/14626268.2023.2174557
• Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of operations research, 308(1), 641-652. https://doi.org/10.1007/s10479-020-03918-9
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
• 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. Management Information Systems Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
• Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanities and Social Sciences Communications, 12(1), 1-21. https://doi.org/10.1057/s41599-025-04787-y
• Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167
• Wu, K., Zhao, Y., Zhu, Q., Tan, X., & Zheng, H. (2011). A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type. International Journal of Information Management, 31(6), 572-581. https://doi.org/10.1016/j.ijinfomgt.2011.03.004
• Xue, L., Mahat, J., Ghazali, N., & Shi, X. (2025). Technology acceptance model (TAM) in artificial intelligence in higher education (AIHEd): A systematic literature review. Journal of Institutional Research South East Asia, 23(1).
• Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., ... & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112. https://doi.org/10.1111/bjet.13370
• Yu, C., Yan, J., & Cai, N. (2024). ChatGPT in higher education: Factors influencing ChatGPT user satisfaction and continued use intention. Frontiers in Education, 9, Article 1354929. https://doi.org/10.3389/feduc.2024.1354929
• Zhang, S., Meng, Z., Chen, B., Yang, X., & Zhao, X. (2021). Motivation, social emotion, and the acceptance of artificial intelligence virtual assistants: Trust-based mediating effects. Frontiers in Psychology, 12, Article 728495. https://doi.org/10.3389/fpsyg.2021.728495