kastamonu education journal Kastamonu Education Journal 2147-9844 Kastamonu University 10.24106/kefdergi.1939356 Instructional Technologies Öğretim Teknolojileri Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective Yüksek Öğretim Öğrencilerinin Üretken Yapay Zekâ Sohbet Robotlarını Kullanımının Belirleyicileri: Genişletilmiş Teknoloji Kabul Modeli (TKM) Bakış Açısı https://orcid.org/0000-0002-3780-3005 Gelibolu Mehmet Fikret HATAY MUSTAFA KEMAL UNIVERSITY, FACULTY OF EDUCATION 04 30 2026 34 2 290 310 11 09 2025 03 26 2026 Copyright © 1995, Kastamonu Education Journal 1995 Kastamonu Education Journal

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.

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.

 Generative artificial intelligence (Gen-AI) Technology acceptance model (TAM) Trust Individual impact Higher education Üretken yapay zekâ Teknoloji kabul modeli (TKM) Güven Bireysel etki Yüksek öğretim 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