Research Paper
Article citation: Kumar, A., & Pathak, A. A. (2026). Algorithmic HRM and generative AI: A multi-level assimilation model of generative AI. Journal of Global Information Management, 34(1), 1–23. https://doi.org/10.4018/JGIM.405241
Abstract
The study examines the effect of technological, organizational, and people factors on generative AI-enabled HRM assimilation. The study also examines how the assimilation process (intention to use, adoption, and routinization) influences employee agility. Using a mixed-methods approach, this study was qualitative in Phase 1 that involved conducting in-depth interviews. The model developed was then empirically validated in Phase 2. The findings of this study revealed that technology infrastructure, task-technology fit, top management support, personal innovativeness, and openness to experience are associated with at least one stage of the assimilation process in the context of generative AI-enabled HRM. Further, the results show that adoption is positively associated with employee agility. The study enriches the existing literature pertaining to generative AI-enabled HRM assimilation and technology adoption. The research also enriches the literature pertaining to the theory of innovation assimilation and the technology-organization-people framework.