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Navigating the Limitations of Algorithmic Management: An Integrative Framework of Sociotechnical Systems Theory (STS) and Strategic HRD

Panpan Zhang et al.

Human Resource Development Review2025https://doi.org/10.1177/15344843251320252article
ABDC B
Weight
0.53

Abstract

This research integrates Sociotechnical Systems (STS) theory with strategic human resource development (SHRD) to address the potential biases and inequities in algorithmic management within workplaces. Given SHRD’s strategic orientation and focus on human development, integrating these frameworks has the potential to achieve both technological efficiency and organizational growth, while also promoting employee well-being. By analyzing the strategic design, operating systems, and work design of STS through an SHRD lens, we propose an STS-SHRD framework highlighting human resource development’s (HRD) strategic role in aligning organizational objectives and technological processes for enhanced algorithmic management. The proposed framework guides the development of both technological integration and employee well-being. This research contributes to ongoing HRD discourse by offering theoretical insights into collaborative approaches that balance organizational effectiveness with human development, while also providing practical strategies for HRD professionals to foster employee engagement, promote equitable organizational practices, and enhance employee well-being.

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https://doi.org/https://doi.org/10.1177/15344843251320252

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@article{panpan2025,
  title        = {{Navigating the Limitations of Algorithmic Management: An Integrative Framework of Sociotechnical Systems Theory (STS) and Strategic HRD}},
  author       = {Panpan Zhang et al.},
  journal      = {Human Resource Development Review},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/15344843251320252},
}

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Evidence weight

0.53

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

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.70 × 0.15 = 0.10
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.