Authors : Dr. Md. Jahangir Sadat; Dr. A. S. M. Sarfaraz Nawaz

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/5h4h3y5r

Scribd : https://tinyurl.com/24fyyf3m

DOI : https://doi.org/10.38124/ijisrt/26jun1055

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid integration of Generative Artificial Intelligence (GenAI) technologies into organizational processes has transformed workplace practices while simultaneously generating concerns among employees regarding job displacement, skill obsolescence, and technological uncertainty. Although Generative AI offers substantial opportunities for enhancing productivity and innovation, employees frequently experience AI-related anxiety that may adversely affect workplace outcomes. Despite growing scholarly attention to AI adoption, limited empirical evidence exists concerning the mechanisms through which organizations can alleviate Generative AI anxiety, particularly in emerging economies such as Bangladesh. This study investigates the influence of digital literacy and self-efficacy on Generative AI anxiety and examines their subsequent effects on employee job performance. Drawing upon Social Cognitive Theory (SCT) and the Technology Acceptance Model (TAM), the study proposes and tests a conceptual framework linking digital literacy, self-efficacy, Generative AI anxiety, and job performance. A quantitative research design was employed, and data were collected through a structured questionnaire from 150 employees working in diverse industries, including banking, telecommunications, information technology, education, and manufacturing organizations in Bangladesh.

Keywords : Generative Artificial Intelligence, AI Anxiety, Digital Literacy, Self-Efficacy, Job Performance, Workplace Technology, Social Cognitive Theory, PLS-SEM.

References :

1. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
2. Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
3. Bipasha, N. J., Bhuyan, M. M., & Muhammad, M. I. (2026). Artificial intelligence understanding to self-efficacy: The mediating roles of use behavior and skills. Journal of Librarianship and Information Science. Advance online publication. https://doi.org/10.1177/03400352261457340
4. Bogart, C., Warrier, A., Agarwal, A., Higashi, R., Zhang, Y., Flot, J., Savelka, J., Burte, H., & Sakr, M. (2025). AI literacy assessment revisited: A task-oriented approach aligned with real-world occupations. arXiv Preprint. https://arxiv.org/abs/2511.05475
5. Chen, Y., Lv, Z., Li, X., Chen, Y., & Li, W. (2026). Artificial intelligence literacy and academic resilience in undergraduate nursing students: The mediating role of self-efficacy and artificial intelligence anxiety. BMC Nursing, 25, 276. https://doi.org/10.1186/s12912-026-04420-6
6. Chen, G., Gully, S. M., & Eden, D. (2001). Validation of a new general self-efficacy scale. Organizational Research Methods, 4(1), 62–83.
7. 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
8. 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
9. Duan, S. X., Deng, H., & Wibowo, S. (2024). Technology affordances for enhancing job performance in digital work. Journal of Computer Information Systems, 64(2), 232–244. https://doi.org/10.1080/08874417.2023.2188497
10. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
11. Haq, F. U., Masood, A., Abbas, M., Ahmed, A., & Ahmed, I. (2026). AI-induced job insecurity and employee burnout: The moderating role of AI self-efficacy in emerging market manufacturing. Journal of Applied Behavioral Science. Advance online publication. https://doi.org/10.1177/01672533261439540
12. Jin, G., Jiang, J., & Liao, H. (2024). The work affective well-being under the impact of AI. Scientific Reports, 14, 25483.
13. Kim, B.J., & Lee, J. (2024). The mental health implications of artificial intelligence adoption: The crucial role of self-efficacy. Humanities and Social Sciences Communications, 11, 1561. https://doi.org/10.1057/s41599-024-04018-w
14. Kınış, Z., Denizli, F., & Demirtaş, Ö. (2026). The mediating role of transformational leadership in the effect of artificial intelligence anxiety and job performance: A field study. BMC Health Services Research, 26, 641. https://doi.org/10.1186/s12913-026-14434-0
15. Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., de Vet, H. C. W., & van der Beek, A. J. (2014). Construct validity of the Individual Work Performance Questionnaire. Journal of Occupational and Environmental Medicine, 56(3), 331–337. https://doi.org/10.1097/JOM.0000000000000113
16. Li, R., Ouyang, S., & Lin, J. (2025). Mediating effect of AI attitudes and AI literacy on the relationship between career self-efficacy and job-seeking anxiety. BMC Psychology, 13, 454. https://doi.org/10.1186/s40359-025-02757-2
17. Liu, X., Zhang, L., & Wei, X. (2025). Generative Artificial Intelligence literacy: Scale development and its effect on job performance. Behavioral Sciences, 15(6), 811.
18. Liu, Y., Sheng, F., & Liu, R. (2025). Generative AI adoption and employee outcomes: A conservation of resources perspective on job crafting, career commitment, and the moderating role of liking of AI. Humanities and Social Sciences Communications, 12, 1376.
19. Marimon, F., Mas-Machuca, M., & Akhmedova, A. (2024). Trusting in generative AI: Catalyst for employee performance and engagement in the workplace. International Journal of Human–Computer Interaction, 41(11), 7076–7091. https://doi.org/10.1080/10447318.2024.2388482
20. Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078.
21. Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
22. Tali, F. (2026). Digital self-efficacy as a foundation for a generative AI usage framework in faculty professional practices. arXiv Preprint. https://arxiv.org/abs/2602.17673
23. Tong, H., Yu, J., & Shou, M. (2025). Mitigating the effect of AI anxiety on employees’ creativity: A social cognitive perspective. Journal of Digital Management, 1(7).
24. Van Laar, E., Van Deursen, A. J. A. M., Van Dijk, J. A. G. M., & De Haan, J. (2017). The relation between 21st-century skills and digital skills: A systematic literature review. Computers in Human Behavior, 72, 577–588. https://doi.org/10.1016/j.chb.2017.03.010
25. Van Laar, E., Van Deursen, A. J. A. M., Van Dijk, J. A. G. M., & De Haan, J. (2020). Determinants of 21st-century digital skills and outcomes for the working professional. Computers in Human Behavior, 72, 577–588.
26. 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
27. Wang, Y.Y., & Wang, Y.S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887
28. Williams, L. J., & Anderson, S. E. (1991). Job satisfaction and organizational commitment as predictors of organizational citizenship and in-role behaviors. Journal of Management, 17(3), 601–617.