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Algorithmic HRM and Generative AI

Aman Kumar & Anil Anand Pathak

Journal of Global Information Management2026https://doi.org/10.4018/jgim.405241article
AJG 2ABDC A
Weight
0.50

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.

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https://doi.org/https://doi.org/10.4018/jgim.405241

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@article{aman2026,
  title        = {{Algorithmic HRM and Generative AI}},
  author       = {Aman Kumar & Anil Anand Pathak},
  journal      = {Journal of Global Information Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/jgim.405241},
}

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Algorithmic HRM and Generative AI

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.