Impact of AI-enabled HR practices on innovative work behavior: Mediating role of workforce agility and moderating role of employee resilience
DOI:
https://doi.org/10.71085/rjhsa.04.03.99Keywords:
Artificial Intelligence, HR Practices, AI-supported HR Practices, Innovative Work Behavior, Workforce Agility, Employee Resilience, Digital TransformationAbstract
Technology, especially in its current combination of artificial intelligence and cyber tools, drones, cryptocurrencies, encrypted platforms and algorithmically-managed social media, has made repeated adaptations to terrorism, all of which have led to a terrorist practice that is vastly different in scale, speed and anonymity, disrupting traditional models of countering terrorism. This article explores the implications of technological advances for terrorist activities by examining the extent to which current governance regimes are flexible enough to address technology-enabled terrorism. A pragmatic mixed-methods design, which uses systematic document analysis, descriptive quantification of patterns of technology use, and qualitative comparative case analysis, analyses 14 documented incidents and campaigns of terror between 2016– 2025. The data sources used were United Nations documents, FATF publications, the Global Terrorism Database, Europol and INTERPOL reports, government security assessments, and peer-reviewed scholarship. While the use of AI is in its early stages, generative systems already influence how propaganda is made, how falsehoods, deceit, and translations are produced, and who the target is. Governance frameworks have been scaled up, including via the United Nations Global Counter-Terrorism Strategy, the FATF virtual-asset standards, Europol and INTERPOL collaboration and coordination, as well as the regulation of online content and by means of initiatives for the governance of AI, but implementation has been partial and non-uniform across regions, and reactive. The article adds an integrated empirical-conceptual approach to understanding the technology of terror, treating it as a collection of individual tools.
Downloads
References
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). Sage Publications.
Dima, J., Gilbert, M. H., Dextras-Gauthier, J., & Giraud, L. (2024). The effects of artificial intelligence on human resource activities and the roles of the human resource triad: Opportunities and challenges. Frontiers in Psychology, 1
Do, H., Chu, L. X., & Shipton, H. (2025). How and when AI-driven HRM promotes employee resilience and adaptive performance: A self-determination theory perspective. Journal of Business Research, 192, 115279.
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 Publications.
Jangbahadur, U., et al. (2024). The effect of AI-enabled HRM dimensions on employee engagement and sustainable organizational performance. Evidence-Based HRM.
Khatoon, U. T., et al. (2025). Technology-driven change in human resource management: A review of digital transformation and workforce adaptation. Administrative Sciences, 15(11).
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson.
Sekaran, U., & Bougie, R. (2020). Research methods for business: A skill building approach (8th ed.). Wiley.
Strohmeier, S., Parry, E., et al. (2024). Responsible artificial intelligence in human resources management: A review of the empirical literature. AI and Ethics, 4, 1185–1200.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533
Xiao, Q., Yan, J., & Bamber, G. J. (2025). How does AI-enabled HR analytics influence employee resilience: Job crafting as a mediator and HRM system strength as a moderator. Personnel Review, 54(3), 824–843.
Creswell, J. W., & Creswell, J. D. (2023). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (6th ed.). Sage Publications.
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 Publications.
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research Methods for Business Students (8th ed.). Pearson.
Sekaran, U., & Bougie, R. (2020). Research Methods for Business: A Skill Building Approach (8th ed.). Wiley.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Rubab Zohrah, Muhammad Mateen Khurram (Author)

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



