Leveraging Artificial Intelligence for Career Development: The Effect of AI Use Intention on Graduate Employability Through Career Adaptability and Job Search Self-Efficacy
DOI:
https://doi.org/10.59261/inkubis.v8i4.534Keywords:
AI Trust, Artificial Intelligence Use Intention, Career Adaptability, Graduate Employability, Job Search Self-EfficacyAbstract
Background: Fresh graduates face an increasingly difficult school-to-work transition, while artificial intelligence (AI) has become a routine study and job-search companion; yet how the intention to use AI translates into employability, and through which mechanisms, remains unclear.
Objective: Grounded in Social Cognitive Theory, this study tests whether behavioral intention to use AI is associated with self-perceived graduate employability directly and indirectly through career adaptability and job search self-efficacy, and whether AI self-efficacy and AI trust moderate these paths.
Methods: A cross-sectional online survey of 612 Indonesian fresh graduates who use AI for career preparation was analyzed with partial least squares structural equation modeling (PLS-SEM).
Results: AI use intention positively predicted career adaptability (β = 0.305) and job search self-efficacy (β = 0.249), and both resources predicted self-perceived employability. The indirect paths through both resources were significant, and a direct effect of AI use intention on employability remained, indicating partial mediation. AI self-efficacy and AI trust showed significant main effects on their resources, while the interaction-related indirect effects were not significant.
Conclusion: AI use intention is associated with self-perceived employability through career adaptability and job search self-efficacy. The cross-sectional findings do not establish causality. Educational institutions and career-service units may use these findings to inform structured AI-supported occupational exploration, skills-gap analysis, résumé refinement, and interview rehearsal, while encouraging critical verification of AI outputs.
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Aburumman, O. J., Omar, K., Al Shbail, M., & Aldoghan, M. (2023). How to Deal with the Results of PLS-SEM? Lecture Notes in Networks and Systems, 495 LNNS. https://doi.org/10.1007/978-3-031-08954-1_101
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action Control: From Cognition to Behavior (pp. 11–39). Springer. https://doi.org/10.1007/978-3-642-69746-3_2
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
Bhati, K., & Sethy, T. P. (2022). Self-Efficacy: Theory to Educational Practice. The International Journal of Indian Psychology, 10(1).
Biramo, Y. B., Merkine Gebresilase, B., Elka, Z. Z., & Demissie, E. B. (2025). The effect of perceived employability on the job search behavior of unemployed youths: the mediating role of job search self-efficacy. International Journal of Adolescence and Youth, 30(1). https://doi.org/10.1080/02673843.2025.2454999
Burhan, Q.-A. (2024). Unraveling the AI enigma: How perceptions of artificial intelligence forge career adaptability through the crucible of career insecurity and skill development. Management Research Review, 48, 470–488. https://doi.org/10.1108/MRR-01-2024-0022
Brown, J. L., McIlveen, P., Perera, H. N., & Hammer, S. J. (2024). Measurement properties of the dispositional measure of employability in Australian university students. Australian Journal of Career Development, 33(1). https://doi.org/10.1177/10384162231223662
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, 43. https://doi.org/10.1186/s41239-023-00411-8
Chiu, M. L. (2025). Exploring user awareness and perceived usefulness of generative AI in higher education: The moderating role of trust. Education and Information Technologies, 30, 21317–21351. https://doi.org/10.1007/s10639-025-13612-7
Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2). https://doi.org/10.1007/s10490-023-09871-y
Chong, A. Y. L., Blut, M., & Zheng, S. (2022). Factors influencing the acceptance of healthcare information technologies: A meta-analysis. Information and Management, 59(3). https://doi.org/10.1016/j.im.2022.103604
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
Dai, Y., Liu, A., & Lim, C. P. (2023). Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education. Procedia CIRP, 119, 84–90. https://doi.org/10.1016/j.procir.2023.05.002
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Fauzi, M. A., Hanafiah, M. H., & Kunjuraman, V. (2024). Tourists’ intention to visit green hotels: building on the theory of planned behaviour and the value-belief-norm theory. Journal of Tourism Futures, 10(2). https://doi.org/10.1108/JTF-01-2022-0008
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley
Fugate, M., & Kinicki, A. J. (2008). A dispositional approach to employability: Development of a measure and test of implications for employee reactions to organizational change. Journal of Occupational and Organizational Psychology, 81(3), 503–527. https://doi.org/10.1348/096317907X241579
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14, 627–660. https://doi.org/10.5465/annals.2018.0057
Habib, S., Vogel, T., Anli, X., & Thorne, E. (2024). How does generative artificial intelligence impact student creativity? Journal of Creativity, 34, 100072. https://doi.org/10.1016/j.yjoc.2023.100072
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31, 2–24. https://doi.org/10.1108/EBR-11-2018-0203
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
Ho, N. T. T., & Le, H. V. (2026). Empowered by AI: Exploring the link between literacy, self-efficacy, and career expectations. Acta Psychologica, 264, 106408. https://doi.org/10.1016/j.actpsy.2026.106408
Hui, X., Reshef, O., & Zhou, L. (2024). The short-term effects of generative artificial intelligence on employment: Evidence from an online labor market. Organization Science, 35, 1977–1989. https://doi.org/10.1287/orsc.2023.18441
Imbrisca, C. I., Herman, R., Nistor, C., & Jula, N. M. (2026). The Theory of Planned Behavior in the Analysis of Green Product Purchase Intention. Sustainability (Switzerland), 18(13). https://doi.org/10.3390/su18136537
Kanfer, R., Wanberg, C. R., & Kantrowitz, T. M. (2001). Job search and employment: A personality-motivational analysis and meta-analytic review. Journal of Applied Psychology, 86(5), 837–855. https://doi.org/10.1037/0021-9010.86.5.837
Kara, A., Orum-Çattık, E., & Eryılmaz, A. (2022). Adaptivity, adaptability, adapting response, and adaptation result: testing with structural equation modelling on pre-service teachers. Current Psychology, 41(7). https://doi.org/10.1007/s12144-021-01975-2
Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29, 1–27.
Koen, J., Klehe, U.-C., Van Vianen, A. E. M., Zikic, J., & Nauta, A. (2010). Job-search strategies and reemployment quality: The impact of career adaptability. Journal of Vocational Behavior, 77(1), 126–139. https://doi.org/10.1016/j.jvb.2010.02.004
Kong, H., Yin, Z., Baruch, Y., & Yuan, Y. (2023). The impact of trust in AI on career sustainability: The role of employee–AI collaboration and protean career orientation. Journal of Vocational Behavior, 146, 103928. https://doi.org/10.1016/j.jvb.2023.103928
Kulal, A. (2025). Cognitive risks of AI: Literacy, trust, and critical thinking. Journal of Computer Information Systems. https://doi.org/10.1080/08874417.2025.2582050
Kumar, P., Chauhan, S., & Awasthi, L. K. (2023). Artificial Intelligence in Healthcare: Review, Ethics, Trust Challenges & Future Research Directions. In Engineering Applications of Artificial Intelligence (Vol. 120). https://doi.org/10.1016/j.engappai.2023.105894
Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a Unifying Social Cognitive Theory of Career and Academic Interest, Choice, and Performance. Journal of Vocational Behavior, 45(1), 79–122. https://doi.org/10.1006/jvbe.1994.1027
Lockey, S., Gillespie, N., Holm, D., & Someh, I. A. (2021). A review of trust in artificial intelligence: Challenges, vulnerabilities and future directions. Proceedings of the 54th Hawaii International Conference on System Sciences, 5463–5472. https://doi.org/10.24251/hicss.2021.664
Mammadov, S., & Avci, A. H. (2025). A Meta-Analytic Review of Personality and Teacher–Student Relationships. Journal of Personality, 93(4). https://doi.org/10.1111/jopy.12986
Marrone, R., Taddeo, V., & Hill, G. (2022). Creativity and artificial intelligence: A student perspective. Journal of Intelligence, 10, 65. https://doi.org/10.3390/jintelligence10030065
Maulana, A., Fenitra, R. M., & Sutrisno, S. (2025). Artificial intelligence, job seeker, and career trajectory: How AI-based learning experiences affect commitment of fresh graduates to be an accountant? Computers and Education: Artificial Intelligence, 8, 100413. https://doi.org/10.1016/j.caeai.2025.100413
Mazhar, B., & Arseven, T. (2026). Students’ writing skills and creativity in the age of artificial intelligence: A systematic review of creativity across diverse AI-supported writing contexts. Thinking Skills and Creativity, 60, 102118. https://doi.org/10.1016/j.tsc.2025.102118
Neves, C., Oliveira, T., Cruz-Jesus, F., & Venkatesh, V. (2025). Extending the unified theory of acceptance and use of technology for sustainable technologies context. International Journal of Information Management, 80. https://doi.org/10.1016/j.ijinfomgt.2024.102838
Nghia, T. L. H., Anh, P. L., & Duyen, N. T. M. (2026). Measuring students’ self-perceived employability capital attainment: the development and validation of a scale. Higher Education. https://doi.org/10.1007/s10734-026-01646-w
Ouyang, W., Shu, X., & Fu, R. (2025). How career adaptability affects university students’ job search behavior and subjective well-being: the role of career choice optimistic bias. BMC Psychology, 13(1). https://doi.org/10.1186/s40359-025-03652-6
Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. In Journal of Strategic Information Systems (Vol. 34, Number 2). https://doi.org/10.1016/j.jsis.2024.101885
Pearson, & Services, A. W. (2026). AI readiness: Building the bridge from higher education to work. https://www.prnewswire.com/apac/news-releases/53-of-employers-cant-find-ai-ready-grads-new-study-maps-six-ways-forward-for-indonesia-302821764.html
Rudolph, C. W., Lavigne, K. N., & Zacher, H. (2017). Career adaptability: A meta-analysis of relationships with measures of adaptivity, adapting responses, and adaptation results. Journal of Vocational Behavior, 98, 17–34. https://doi.org/10.1016/j.jvb.2016.09.002
Rothwell, A., & Arnold, J. (2007). Self-perceived employability: Development and validation of a scale. Personnel Review, 36(1), 23–41. https://doi.org/10.1108/00483480710716704
Rothwell, A., Herbert, I., & Rothwell, F. (2008). Self-perceived employability: Construction and initial validation of a scale for university students. Journal of Vocational Behavior, 73(1), 1–12. https://doi.org/10.1016/j.jvb.2007.12.001
Saks, A. M., & Ashforth, B. E. (1999). Effects of Individual Differences and Job Search Behaviors on the Employment Status of Recent University Graduates. Journal of Vocational Behavior, 54(2), 335–349. https://doi.org/10.1006/jvbe.1998.1665
Saks, A. M., Zikic, J., & Koen, J. (2015). Job search self-efficacy: Reconceptualizing the construct and its measurement. Journal of Vocational Behavior, 86, 104–114. https://doi.org/10.1016/j.jvb.2014.11.007
Savickas, M. L. (2005). The theory and practice of career construction. In S. D. Brown & R. W. Lent (Eds.), Career Development and Counseling: Putting Theory and Research to Work (pp. 42–70). John Wiley & Sons.
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. https://doi.org/10.1016/j.jvb.2012.01.011
Shen, D., Xu, M., Meng, X., Sun, J., & Zhou, Z. (2026). How can the acceptance of artificial intelligence be transformed into employability? The sequential effect of a positive attitude toward artificial intelligence and career adaptability. Frontiers in Psychology, 17, 1808195. https://doi.org/10.3389/fpsyg.2026.1808195
Shen, Y., Wang, Z., Ren, T., Wen, J., Li, J., & Tang, T. (2025). Recent Advances in Microfluidic Impedance Detection: Principle, Design and Applications. In Micromachines (Vol. 16, Number 6). https://doi.org/10.3390/mi16060683
Statistik, B. P. (2026). Keadaan Angkatan Kerja di Indonesia Februari 2026. https://www.bps.go.id/id/publication/2026/06/26/96b31cf730df9e84f17bfe03/keadaan-angkatan-kerja-di-
Twyman, N. W., Pentland, S. J., & Spitzley, L. (2020). Design principles for signal detection in modern job application systems: Identifying fabricated qualifications. Journal of Management Information Systems, 37, 849–874. https://doi.org/10.1080/07421222.2020.1790201
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. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & 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. https://doi.org/10.2307/41410412
Wanberg, C. R. (2012). The Individual Experience of Unemployment. Annual Review of Psychology, 63, 369–396. https://doi.org/10.1146/annurev-psych-120710-100500
Wang, D., & Li, Y. (2024). Career construction theory: tools, interventions, and future trends. In Frontiers in Psychology (Vol. 15). https://doi.org/10.3389/fpsyg.2024.1381233
Wang, H., Fu, F., Li, L., & Liu, M. (2026). How and when does artificial intelligence use enhance student creativity? The mediating role of knowledge-based dynamic capabilities. Frontiers in Psychology, 17, 1861541. https://doi.org/10.3389/fpsyg.2026.1861541
Whipple, M. O., Bergouignan, A., & Hooker, S. A. (2023). Development and Initial Validation of a Measure to Assess Self-Efficacy to Reduce Sedentary Behavior. Medicine and Science in Sports and Exercise, 55(10). https://doi.org/10.1249/MSS.0000000000003215
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