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Configurational effects of personal innovativeness, self-efficacy, and perceived risk on AI adoption in media students
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  • Open access
  • Published: 19 January 2026

Configurational effects of personal innovativeness, self-efficacy, and perceived risk on AI adoption in media students

  • Yanling Lan1,
  • Sihang Liu1,
  • Hao Chen1 &
  • …
  • Linjie Xia1 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Business and management
  • Cultural and media studies
  • Education
  • Information systems and information technology
  • Science, technology and society

Abstract

Generative artificial intelligence (G-AI) is driving systemic transformations in the media industry, redefining the cultivation paradigm of media majors in higher education. This shift necessitates that future media professionals integrate AI thinking and master the application of digital intelligence technologies. To address the knowledge gap in understanding media students’ adoption of AI technologies, this study extends the classical Technology Acceptance Model by incorporating personal innovativeness, AI self-efficacy, and perceived risk, thereby constructing a new and comprehensive model. A survey of 588 media students was conducted, and the data were analyzed using PLS-SEM and fsQCA. The results indicate that: (1) personal innovativeness and AI self-efficacy significantly enhance both perceived usefulness and perceived ease of use; (2) perceived usefulness and perceived ease of use are core determinants of AI behavioral intention, mediating the effects of personal innovativeness and AI self-efficacy; (3) perceived risks can negatively moderate the effect of perceived usefulness and perceived ease of use on AI behavioral intention; (4) AI behavioral intention is shaped not by a single linear determinant but by intersecting configurations of factors, leading to three distinct enhancement pathways: self-driven, efficacy-oriented, and risk-resistant. This study advances the Technology Acceptance Model by integrating psychological and risk-based constructs, offering theoretical insights into the multidimensional driving forces of AI adoption and practical strategies for fostering AI competencies in media students.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Møller, L. A., Skovsgaard, M. & de Vreese, C. Reinforce, readjust, reclaim: how artificial intelligence impacts journalism’s professional claim. Journalism 26, 1373–1390 (2025).

    Google Scholar 

  2. Monti, M. Automated journalism and freedom of information: ethical and Juridical problems related to AI in the press field. Opinio Juris Comparatione. 1, 2018 (2019).

    Google Scholar 

  3. Porlezza, C. Promoting responsible AI: A European perspective on the governance of artificial intelligence in media and journalism. Communications 48, 370–394 (2023).

    Google Scholar 

  4. Whyte, C. Deepfake news: AI-enabled disinformation as a multi-level public policy challenge. J. Cyber Policy. 5, 199–217 (2020).

    Google Scholar 

  5. González-Arias, C., López-García, X. & ChatGPT Stream of opinion in five newspapers in the first 100 days since its launch. Profesional de la Información 32, 10.3145/epi.sep.24 (2023).

  6. Stray, J. Making artificial intelligence work for investigative journalism. Algorithms, automation, and news, 97–118 (2021).

  7. Borchardt, A. Go, robots, go! The value and challenges of artificial intelligence for local journalism. Digit. Journalism. 10, 1919–1924 (2022).

    Google Scholar 

  8. Wenger, D., Hossain, M. S. & Senseman, J. R. AI and the impact on journalism education. Journalism Mass. Communication Educ. 80, 97–114 (2025).

    Google Scholar 

  9. Kuai, J. Unravelling copyright dilemma of AI-generated news and its implications for the institution of journalism: the cases of US, EU, and China. new. Media Soc. 26, 5150–5168 (2024).

    Google Scholar 

  10. Gomez-de-Agreda, A., Feijoo, C. & Salazar-Garcia I.-A. A new taxonomy for image use in the intentional shaping of the digital narrative: deep fakes and artificial intelligence. PROFESIONAL DE LA. INFORMACION 30 (2021).

  11. Brennen, J. S., Howard, P. N. & Nielsen, R. K. What to expect when you’re expecting robots: Futures, expectations, and pseudo-artificial general intelligence in UK news. Journalism 23, 22–38 (2022).

    Google Scholar 

  12. Gutierrez Lopez, M. et al. A question of design: strategies for embedding AI-driven tools into journalistic work routines. Digit. Journalism. 11, 484–503 (2023).

    Google Scholar 

  13. Mitova, E. et al. Exploring users’ desire for transparency and control in news recommender systems: A five-nation study. Journalism 25, 2001–2021 (2024).

    Google Scholar 

  14. Gil-Ruiz, P. & Dominguez-Lloria, S. The generative artificial intelligence of images as a collaborator for creativity: a group case study. ARTE INDIVIDUO Y SOCIEDAD. 37, 339–351 (2025).

    Google Scholar 

  15. Rodway, P. & Schepman, A. The impact of adopting AI educational technologies on projected course satisfaction in university students. Computers Education: Artif. Intell. 5, 100150 (2023).

    Google Scholar 

  16. Oc, Y., Gonsalves, C. & Quamina, L. T. Generative AI in higher education assessments: examining risk and tech-savviness on student’s adoption. J. Mark. Educ. 47, 138–155 (2025).

    Google Scholar 

  17. Ma, S. & Lei, L. The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching. Asia Pac. J. Educ. 44, 94–111 (2024).

    Google Scholar 

  18. Al-Adwan, A. S. et al. Extending the TAM (TAM) to predict university students’ intentions to use metaverse-based learning platforms. Educ. Inform. Technol. 28, 15381–15413 (2023).

    Google Scholar 

  19. Douglass, R. B. (JSTOR, (1977).

  20. Davis, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q.13, 319–340 (1989).

  21. Venkatesh, V. & Davis, F. D. A model of the antecedents of perceived ease of use: development and test. Decis. Sci. 27, 451–481 (1996).

    Google Scholar 

  22. Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. User acceptance of information technology: toward a unified view. MIS Q.27, 425–478 (2003).

  23. Cai, H. Examining social e-commerce platforms by mediating the effect of perceived usefulness and perceived trust using the TAM. J. Organizational End. User Comput. (JOEUC). 34, 1–20 (2022).

    Google Scholar 

  24. Lin, W. Y., Chou, W. C., Tsai, T. H., Lin, C. C. & Lee, M. Y. Development of a wearable instrumented vest for posture monitoring and system usability verification based on the TAM. Sensors 16, 2172 (2016).

    Google Scholar 

  25. Fareed, A. S. & Kirkil, G. Integrating TAM with UTAUT to increase the explanatory power of the effect of HCI on students’ intention to use E-Learning system and perceive success. IEEE Access.13, 20720–20739 (2025).

  26. Granić, A. Educational technology adoption: A systematic review. Educ. Inform. Technol. 27, 9725–9744 (2022).

    Google Scholar 

  27. Zou, M. & Huang, L. To use or not to use? Understanding doctoral students’ acceptance of ChatGPT in writing through TAM. Front. Psychol. 14, 1259531 (2023).

    Google Scholar 

  28. Tahat, D. N. et al. Technology enhanced learning in undergraduate level education: a case study of students of mass communication. Sustainability 15, 15280 (2023).

    Google Scholar 

  29. Chen, J., Li, R., Gan, M., Fu, Z. & Yuan, F. Public acceptance of driverless buses in China: an empirical analysis based on an extended UTAUT model. Discrete Dynamics in Nature and Society 4318182 (2020). 

  30. Lu, J., Liu, C., Yu, C. S. & Wang, K. Determinants of accepting wireless mobile data services in China. Inf. Manag. 45, 52–64 (2008).

    Google Scholar 

  31. Williams, D. M. & Rhodes, R. E. The confounded AI self-efficacy construct: conceptual analysis and recommendations for future research. Health Psychol. Rev. 10, 113–128 (2016).

    Google Scholar 

  32. Montag, C., Kraus, J., Baumann, M. & Rozgonjuk, D. The propensity to trust in (automated) technology mediates the links between technology AI self-efficacy and fear and acceptance of artificial intelligence. Computers Hum. Behav. Rep. 11, 100315 (2023).

    Google Scholar 

  33. Kim, B. J. & Kim, M. J. The influence of work overload on cybersecurity behavior: A moderated mediation model of psychological contract breach, burnout, and AI self-efficacy in AI learning such as ChatGPT. Technol. Soc. 77, 102543 (2024).

    Google Scholar 

  34. Hwang, J., Kim, J., Joo, K. H. & Choe, J. Y. An integrated model of artificially intelligent (AI) facial recognition technology adoption based on perceived risk theory and extended TPB: A comparative analysis of US and South Korea. J. Hospitality Mark. Manage. 33, 1071–1099 (2024).

    Google Scholar 

  35. Lee, J. H. & Song, C. H. Effects of trust and perceived risk on user acceptance of a new technology service. Social Behav. Personality: Int. J. 41, 587–597 (2013).

    Google Scholar 

  36. Duffett, R., Zaharia, R. M., Edu, T., Constantinescu, R. & Negricea, C. Exploring The antecedents of artificial intelligence products’usage. The case of business students. Amfiteatru Economic. 26, 106–125 (2024).

    Google Scholar 

  37. Rogers, E. M. Schuster Diffusion of Innovations 5th edn. (Free, 2003).

    Google Scholar 

  38. Van Raaij, E. M. & Schepers, J. J. The acceptance and use of a virtual learning environment in China. Comput. Educ. 50, 838–852 (2008).

    Google Scholar 

  39. Gunness, A., Matanda, M. J. & Rajaguru, R. Effect of student responsiveness to instructional innovation on student engagement in semi-synchronous online learning environments: the mediating role of personal technological innovativeness and perceived usefulness. Comput. Educ. 205, 104884 (2023).

    Google Scholar 

  40. Wang, Y. Y. & Chuang, Y. W. Artificial intelligence AI self-efficacy: scale development and validation. Educ. Inform. Technol. 29, 4785–4808 (2024).

    Google Scholar 

  41. Mouakket, S. Factors influencing continuance intention to use social network sites: the Facebook case. Comput. Hum. Behav. 53, 102–110 (2015).

    Google Scholar 

  42. Li, K. Determinants of college students’ actual use of AI-based systems: an extension of the TAM. Sustainability 15, 5221 (2023).

    Google Scholar 

  43. Algerafi, M. A., Zhou, Y., Alfadda, H. & Wijaya, T. T. Understanding the factors influencing higher education students’ intention to adopt artificial intelligence-based robots. Ieee Access. 11, 99752–99764 (2023).

    Google Scholar 

  44. Sánchez-Prieto, J. C., Izquierdo-Álvarez, V., del Moral-Marcos, M. T. & Martínez-Abad, F. Generative artificial intelligence for self-learning in higher education: design and validation of an example machine. RIED-Revista Iberoamericana De Educación Distancia 28 (2025).

  45. Ateş, H. & Gündüzalp, C. Proposing a conceptual model for the adoption of artificial intelligence by teachers in STEM education. Interact. Learn. Environ.33, 1–27 (2025).

  46. Warshaw, P. R. & Davis, F. D. Disentangling behavioral intention and behavioral expectation. J. Exp. Soc. Psychol. 21, 213–228 (1985).

    Google Scholar 

  47. Venkatesh, V. & Davis, F. D. A theoretical extension of the TAM: four longitudinal field studies. Manage. Sci. 46, 186–204 (2000).

    Google Scholar 

  48. Zhao, Y., Li, Y., Xiao, Y., Chang, H. & Liu, B. Factors influencing the acceptance of ChatGPT in high education: an integrated model with PLS-SEM and FsQCA approach. Sage Open. 14, 21582440241289835 (2024).

    Google Scholar 

  49. Ayanwale, M. A. & Molefi, R. R. Exploring intention of undergraduate students to embrace chatbots: from the vantage point of Lesotho. Int. J. Educational Technol. High. Educ. 21, 20 (2024).

    Google Scholar 

  50. Hair, J. F. Jr et al. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook (Springer Nature, 2021).

  51. Sarstedt, M. et al. Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychol. Mark. 39, 1035–1064 (2022).

    Google Scholar 

  52. Hair, J. F. et al. Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice. Bus. Res. 12, 115–142 (2019).

    Google Scholar 

  53. Donath, L. et al. Perceptions’ Investigation Regarding the Need for Upskilling in Remote Education: A PLS-SEM Analysis Vol. 58 (Economic Computation & Economic Cybernetics Studies & Research, 2024).

  54. Chanda, R. C., Vafaei-Zadeh, A., Hanifah, H. & Ramayah, T. Investigating factors influencing individual user’s intention to adopt cloud computing: a hybrid approach using PLS-SEM and FsQCA. Kybernetes 53, 4470–4501 (2024).

    Google Scholar 

  55. Greckhamer, T., Furnari, S., Fiss, P. C. & Aguilera, R. V. Studying configurations with qualitative comparative analysis: best practices in strategy and organization research. Strategic Organ. 16, 482–495 (2018).

    Google Scholar 

  56. Pappas, I. O. & Woodside, A. G. Fuzzy-set qualitative comparative analysis (fsQCA): guidelines for research practice in information systems and marketing. Int. J. Inf. Manag. 58, 102310 (2021).

    Google Scholar 

  57. Liu, J., Chen, X., Liu, C. & Han, P. Factors influencing adoption intentions to use AIGC for health information: findings from SEM and FsQCA. Front. Public. Health. 13, 1525879 (2025).

    Google Scholar 

  58. Li, H. et al. Unveiling the complexity of designers’ intention to use generative AI in corporate product design: A grounded theory and FsQCA. Systems 13, 275 (2025).

    Google Scholar 

  59. Tang, Y. & Su, L. Graduate education in China Meets AI: key factors for adopting AI-generated content tools. Libri 75, 81–96 (2025).

    Google Scholar 

  60. Zhou, T., Wu, X. & Examining Generative, A. I. User disclosure intention: A perceived affordance perspective. J. Theoretical Appl. Electron. Commer. Res. 20, 99 (2025).

    Google Scholar 

  61. Faqih, K. M., Jaradat, M. I. R. M. & Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: perspective from a developing country. Technol. Soc. 67, 101787 (2021).

    Google Scholar 

  62. Saleh, S. S., Nat, M. & Aqel, M. Sustainable adoption of e-learning from the TAM perspective. Sustainability 14, 3690 (2022).

    Google Scholar 

  63. Zhang, F., Sun, S., Liu, C. & Chang, V. Consumer innovativeness, product innovation and smart toys. Electron. Commer. Res. Appl. 41, 100974 (2020).

    Google Scholar 

  64. Xia, T., Lin, X., Sun, Y. & Liu, T. An empirical study of the factors influencing users’ intention to use automotive AR-HUD. Sustainability 15, 5028 (2023).

    Google Scholar 

  65. Yang, F., Ren, L. & Gu, C. A study of college students’ intention to use metaverse technology for basketball learning based on UTAUT2. Heliyon 8 (2022).

  66. Shen, S., Xu, K., Sotiriadis, M. & Wang, Y. Exploring the factors influencing the adoption and usage of augmented reality and virtual reality applications in tourism education within the context of COVID-19 pandemic. J. Hospitality Leisure Sport Tourism Educ. 30, 100373 (2022).

    Google Scholar 

  67. Zou, C., Li, P. & Jin, L. Integrating smartphones in EFL classrooms: students’ satisfaction and perceived learning performance. Educ. Inform. Technol. 27, 12667–12688 (2022).

    Google Scholar 

  68. Chen, S. C. & Chen, H. H. The empirical study of customer satisfaction and continued behavioural intention towards self-service banking: technology readiness as an antecedent. Int. J. Electron. Finance. 3, 64–76 (2009).

    Google Scholar 

  69. Nunnally, J. C. Psychometric theory—25 years ago and now. Educational Researcher. 4, 7–21 (1975).

    Google Scholar 

  70. DeVellis, R. F. & Thorpe, C. T. Scale Development: Theory and Applications (Sage, 2021).

  71. Hair, J. F. Multivariate data analysis. (2009).

  72. Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S. & Wang, L. C. Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pac. J. Manage. 41, 745–783 (2024).

    Google Scholar 

  73. Astuti, C. C. PLS-SEM analysis to know factors affecting the interest of buying Halal food in Muslim students. Jurnal Varian. 4, 141–152 (2021).

    Google Scholar 

  74. Fornell, C. & Larcker, D. F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18, 39–50 (1981).

    Google Scholar 

  75. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. & Tatham, R. L. Análise Multivariada De Dados (Bookman editora, 2009).

  76. Demler, O. V., Paynter, N. P. & Cook, N. R. Tests of calibration and goodness-of‐fit in the survival setting. Stat. Med. 34, 1659–1680 (2015).

    Google Scholar 

  77. Li, W. A study on factors influencing designers’ behavioral intention in using AI-generated content for assisted design: perceived anxiety, perceived risk, and UTAUT. Int. J. Human–Computer Interact. 41, 1064–1077 (2025).

    Google Scholar 

  78. Shmueli, G., Ray, S., Estrada, J. M. V. & Chatla, S. B. The elephant in the room: predictive performance of PLS models. J. Bus. Res. 69, 4552–4564 (2016).

    Google Scholar 

  79. He, S. & Ren, Y. Exploring pre-service music teachers’ acceptance of generative artificial intelligence: a PLS-SEM-ANN approach. Front. Psychol. 16, 1571279 (2025).

    Google Scholar 

  80. Shmueli, G. et al. Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. Eur. J. Mark. 53, 2322–2347 (2019).

    Google Scholar 

  81. Chen, N. H. Extending a TAM–TTF model with perceptions toward telematics adoption. Asia Pac. J. Mark. Logistics. 31, 37–54 (2019).

    Google Scholar 

  82. Zehir, C. Personal innovativeness and perceived system quality for information system success: the role of diffusability of innovation. Tehnički Vjesn. 28, 1717–1726 (2021).

    Google Scholar 

  83. Zhang, Y., Yu, X., Cai, N. & Li, Y. Analyzing the employees’ new media use in the energy industry: the role of creative AI self-efficacy, perceived usefulness and leaders’ use. Sustainability 12, 967 (2020).

    Google Scholar 

  84. Joo, Y. J., Park, S. & Lim, E. Factors influencing preservice teachers’ intention to use technology: TPACK, teacher AI self-efficacy, and TAM. J. Educational Technol. Soc. 21, 48–59 (2018).

    Google Scholar 

  85. Hoang, Y. N. et al. The moderating effect of economic development levels on the adoption of eNutrition technologies in medical education: A multinational survey across six Asian countries. Digit. Health. 11, 20552076251350805 (2025).

    Google Scholar 

  86. Mohamed Eldakar, M. A., Shehata, K. & Abdelrahman Ammar, A. S. A. M. What motivates academics in Egypt toward generative AI tools? An integrated model of TAM, SCT, UTAUT2, perceived ethics, and academic integrity. Inform. Dev.41, 02666669251314859 (2025).

  87. Humbani, M. & Wiese, M. An integrated framework for the adoption and continuance intention to use mobile payment apps. Int. J. Bank. Mark. 37, 646–664 (2019).

    Google Scholar 

  88. Tseng, C. H., Hsu, C. H., Liu, J. W. & Wang, C. T. The impact of online teaching in behavior intention for college students in Taiwan. Front. Psychol. 13, 911262 (2022).

    Google Scholar 

  89. Çelik, K. & Ayaz, A. Evaluation of metaverse use intention in software education of university students: combining TAM with external variables. Education Tech. Research Dev. 73, 641–662 (2025).

    Google Scholar 

  90. Thüs, D., Malone, S. & Brünken, R. Exploring generative AI in higher education: a RAG system to enhance student engagement with scientific literature. Front. Psychol. 15, 1474892 (2024).

    Google Scholar 

  91. Mun, I. B. & Hwang, K. H. Exploring the influence of prompt AI self-efficacy: accurate and customized Information, perceived ease of use, Satisfaction, and continuance intention to use ChatGPT. Int. J. Human–Computer Interact.41, 1–12 (2025).

  92. Pieters, C., Pieters, R. & Lemmens, A. Six methods for latent moderation analysis in marketing research: A comparison and guidelines. J. Mark. Res. 59, 941–962 (2022).

    Google Scholar 

  93. Frazier, P. A., Tix, A. P. & Barron, K. E. Testing moderator and mediator effects in counseling psychology research. J. Couns. Psychol. 51, 115 (2004).

    Google Scholar 

  94. Ye, X., Yan, Y., Li, J. & Jiang, B. Privacy and personal data risk governance for generative artificial intelligence: A Chinese perspective. Telecomm. Policy. 48, 102851 (2024).

    Google Scholar 

  95. Yang, X., Ding, J., Chen, H. & Ji, H. Factors affecting the use of artificial intelligence generated content by subject librarians: A qualitative study. Heliyon 10, e29584 (2024).

  96. Ragin, C. C. Redesigning Social Inquiry: Fuzzy Sets and Beyond (University of Chicago Press, 2009).

  97. Shah, S. K. & Corley, K. G. Building better theory by bridging the quantitative–qualitative divide. J. Manage. Stud. 43, 1821–1835 (2006).

    Google Scholar 

  98. Schneider, C. Q. & Wagemann, C. Set-theoretic Methods for the Social Sciences: A Guide To Qualitative Comparative Analysis (Cambridge University Press, 2012).

  99. Woodside, A. G. Moving Beyond Multiple Regression Analysis To Algorithms: Calling for Adoption of a Paradigm Shift from Symmetric To Asymmetric Thinking in Data Analysis and Crafting Theory Vol. 66, 463–472 (Elsevier, 2013).

  100. Misangyi, V. F. et al. Embracing causal complexity: the emergence of a neo-configurational perspective. J. Manag. 43, 255–282 (2017).

    Google Scholar 

  101. Ragin, C. C. Set relations in social research: evaluating their consistency and coverage. Political Anal. 14, 291–310 (2006).

    Google Scholar 

  102. Crilly, D., Zollo, M. & Hansen, M. T. Faking it or muddling through? Understanding decoupling in response to stakeholder pressures. Acad. Manag. J. 55, 1429–1448 (2012).

    Google Scholar 

  103. Rihoux, B. & Ragin, C. C. Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques Vol. 51 (Sage, 2009).

  104. Price, E., Ottati, V., Wilson, C. & Kim, S. Open-minded cognition. Pers. Soc. Psychol. Bull. 41, 1488–1504 (2015).

    Google Scholar 

  105. Jahanmir, S. F., Silva, G. M., Gomes, P. J. & Gonçalves, H. M. Determinants of users’ continuance intention toward digital innovations: are late adopters different? J. Bus. Res. 115, 225–233 (2020).

    Google Scholar 

  106. Chatterjee, S., Rana, N. P., Dwivedi, Y. K. & Baabdullah, A. M. Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technol. Forecast. Soc. Chang. 170, 120880 (2021).

    Google Scholar 

  107. Jiang, H., Islam, A. A., Gu, X. & Spector, J. M. Online learning satisfaction in higher education during the COVID-19 pandemic: A regional comparison between Eastern and Western Chinese universities. Educ. Inform. Technol. 26, 6747–6769 (2021).

    Google Scholar 

  108. Alyoussef, I. Y., Drwish, A. M., Albakheet, F. A. & Alhajhoj, R. H. AI adoption for collaboration: factors influencing inclusive learning adoption in higher education. IEEE Access.13, 81690–81713 (2025).

  109. Uddin, M. N., Low, T., Afjalur Rahman, W. & Mokhtar, S. M. In Proceedings of the International Conference on Business, Management and Leadership. 1–19.

  110. Abdullah, F., Ward, R. & Ahmed, E. Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (PEOU) and perceived usefulness (PU) of e-portfolios. Comput. Hum. Behav. 63, 75–90 (2016).

    Google Scholar 

  111. Xu, J., Zhang, X., Li, H., Yoo, C. & Pan, Y. Is everyone an artist? A study on user experience of AI-based painting system. Appl. Sci. 13, 6496 (2023).

    Google Scholar 

  112. Sánchez-Prieto, J. C., Cruz-Benito, J., Therón Sánchez, R. & García-Peñalvo, F. J. Assessed by machines: development of a TAM-based tool to measure AI-based assessment acceptance among students. Int. J. Interact. Multimedia Artif. Intell. 6, 80 (2020).

    Google Scholar 

  113. Hansen, J. M., Saridakis, G. & Benson, V. Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Comput. Hum. Behav. 80, 197–206 (2018).

    Google Scholar 

  114. Man, S. S., Xiong, W., Chang, F. & Chan, A. H. Critical factors influencing acceptance of automated vehicles by Hong Kong drivers. IEEE Access. 8, 109845–109856 (2020).

    Google Scholar 

  115. Jaakkola, M. Journalists as media educators: journalistic media education as inclusive boundary work. Journalism Pract. 16, 1265–1285 (2022).

    Google Scholar 

  116. Morris, K. & Yeoman, F. Teaching future journalists the news: the role of journalism educators in the news literacy movement. Journalism Pract. 17, 1573–1590 (2023).

    Google Scholar 

  117. Nichols, T. P. & LeBlanc, R. J. Media education and the limits of literacy: ecological orientations to performative platforms. Curriculum Inq. 51, 389–412 (2021).

    Google Scholar 

  118. Mohebzadeh, Z., Emamjomeh, S. M. R., Assareh, A. & Hamidi, F. A comparative study of media literacy education curriculum in Canada, Iran, and the united States. Iran. J. Comp. Educ. 3, 737–756 (2020).

    Google Scholar 

  119. Wang, W. & Liu, Z. Using artificial intelligence-based collaborative teaching in media learning. Front. Psychol. 12, 713943 (2021).

    Google Scholar 

  120. Lopezosa, C., Codina, L., Pont-Sorribes, C. & Vállez, M. Use of generative artificial intelligence in the training of journalists: challenges, uses and training proposal. Profesional De la. información 32 (2023).

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Acknowledgements

The authors would like to thank all the participants who completed the questionnaire in this study. All respondents understood the purpose and content of the research and voluntarily participated in completing the questionnaire with informed consent.

Funding

The work was supported by the Innovation Strategy Research Project in Fujian Province of China (No.2024R0110).

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  1. School of Film and Communication, Xiamen University of Technology, Xiamen, China

    Yanling Lan, Sihang Liu, Hao Chen & Linjie Xia

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Author Contributions: Conceptualization, Yanling Lan. Sihang Liu.methodology, Yanling Lan.; software, Sihang Liu.; validation, Hao Chen., Yanling Lan and Sihang Liu.; formal analysis, Yanling Lan.; resources, Hao Chen.; data curation, Yanling Lan; writing—original draft preparation, Yanling Lan, Sihang Liu and Linjie Xia; writing—review and editing, Hao Chen; visualization, Hao Chen.; All authors have read and agreed to the published version of the manuscript.

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Correspondence to Hao Chen.

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Lan, Y., Liu, S., Chen, H. et al. Configurational effects of personal innovativeness, self-efficacy, and perceived risk on AI adoption in media students. Sci Rep (2026). https://doi.org/10.1038/s41598-026-36538-7

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  • Received: 11 September 2025

  • Accepted: 13 January 2026

  • Published: 19 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-36538-7

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Keywords

  • Media education
  • Artificial intelligence(AI)
  • Technology acceptance model (TAM)
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