Effect of AI Technology Acceptance and Use on Behavioral Intentions and Career Adaptability

Authors

  • Tseng-Chung Tang College of Management, National Formosa University, Yunlin, Taiwan
  • Li-Chiu Chi College of Applied Arts and Sciences, National Formosa University, Yunlin, Taiwan,
  • Eugene Tang Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA

DOI:

https://doi.org/10.21834/e-bpj.v10iSI27.6835

Abstract

This study explores how UTAUT2 factors influence college students’ intentions to adopt AI and their career adaptability. Survey data from 327 students show that Performance Expectancy, Hedonic Motivation, and Habit significantly impact AI adoption, while Effort Expectancy, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit influence career adaptability. The findings suggest that fostering positive attitudes toward AI is essential. To prepare students for an AI-driven workforce, the study recommends integrating AI into curricula, providing necessary resources, and promoting collaborative learning to support skill development and adaptability in a rapidly evolving technological landscape.

References

Ayanwale, M. A., & Ndlovu, M. (2024). Investigating Factors of Students’ Behavioral Intentions to Adopt Chatbot Technologies in Higher Education: Perspective from Expanded Diffusion Theory of Innovation. Computers in Human Behavior Reports, 14, 100396. DOI: https://doi.org/10.1016/j.chbr.2024.100396

Chanda, T., Sain, Z., Shogbesan, Y., Phiri, E., & Akpan, W. (2024). Digital Literacy in Education: Preparing Students for the Future Workforce. International Journal of Research, 11(8), 327-344. DOI: https://doi.org/10.30954/2348-7437.1.2024.6

Cortez, P. M., Ong, A., Diaz, J., German, J.D., & Jagdeep, S. (2024). Analyzing Preceding Factors Affecting Behavioral Intention on Communicational Artificial Intelligence as an Educational Tool. Heliyon, 10(3), e25896. DOI: https://doi.org/10.1016/j.heliyon.2024.e25896

Darioshi, R., & Lahav, E. (2021). The Impact of Technology on the Human Decision‐Making Process. Human Behavior and Emerging Technologies, 3(3), 391-400. DOI: https://doi.org/10.1002/hbe2.257

Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly: Management Information Systems, 13(3), 319-339. DOI: https://doi.org/10.2307/249008

Decius, J., Knappstein, M., & Klug, K. (2023). Which Way of Learning Benefits Your Career? The Role of Different Forms of Work-Related Learning for Different Types of Perceived Employability. European Journal of Work and Organizational Psychology, 33(1), 24-39. DOI: https://doi.org/10.1080/1359432X.2023.2191846

Hajjar, S. (2018). Statistical Analysis: Internal-Consistency Reliability and Construct Validity. International Journal of Quantitative and Qualitative Research Methods, 6(1), 27-38.

Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the Role of Digital Technologies in Education: A Review. Sustainable Operations and Computers, 3(4), 275-285. DOI: https://doi.org/10.1016/j.susoc.2022.05.004

Jivtode, M. L. (2024). Impact of Artificial Intelligence (AI) in Education on Students’ Academic Development: Present and Future Prospects. International Journal of Advanced Research in Science, Communication and Technology, 4(4), 712-718.

Muthèn, B., & Kaplan, D. A. (1985). Comparison of Some Methodologies for the Factor Analysis of Non-Normal Likert Variables. British Journal of Mathematical and Statistical Psychology, 38(2), 171-189. DOI: https://doi.org/10.1111/j.2044-8317.1985.tb00832.x

Rashid, A., & Kausik, A. (2024). AI Revolutionizing Industries Worldwide: A Comprehensive Overview of Its Diverse Applications. Hybrid Advances,.7(7), 100277. DOI: https://doi.org/10.1016/j.hybadv.2024.100277

Savickas, M. L. (1997). Career Adaptability: An Integrative Construct for Life-Span, Life-Space Theory. The Career Development Quarterly, 45(3), 247-259. DOI: https://doi.org/10.1002/j.2161-0045.1997.tb00469.x

Savickas, M. L., & Porfeli, E. J. (2012). Career Adapt-Abilities Scale: Construction, Reliability, and Measurement Equivalence Across 13 Countries. Journal of Vocational Behavior, 80(3), 661-673. DOI: https://doi.org/10.1016/j.jvb.2012.01.011

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478. DOI: https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J.Y., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178. DOI: https://doi.org/10.2307/41410412

Vieriu, A. M., & Petrea, G. (2025). The Impact of Artificial Intelligence (AI) on Students’ Academic Development. Education Sciences, 15(3), 343. DOI: https://doi.org/10.3390/educsci15030343

Zacher, H. (2014). Individual Difference Predictors of Change in Career Adaptability Over Time. Journal of Vocational Behavior, 84(2), 188-198. DOI: https://doi.org/10.1016/j.jvb.2014.01.001

Downloads

Published

2025-04-15

How to Cite

Tang, T.-C., Chi, L.-C., & Tang, E. (2025). Effect of AI Technology Acceptance and Use on Behavioral Intentions and Career Adaptability. Environment-Behaviour Proceedings Journal, 10(SI27), 195–200. https://doi.org/10.21834/e-bpj.v10iSI27.6835

Issue

Vol. 10 No. SI27 (2025): Apr. The 6th International Conference of Information Science, ICIS2025PortDickson, 27-28 Jan 2025

Section

Technology-related Environment

License

Copyright (c) 2025 Tseng-Chung Tang, Li-Chiu Chi, Eugene Tang

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC