Human Resource Analytics (HRA) is increasingly recognized for enhancing workforce planning, talent acquisition, and employee engagement through data-driven insights. While existing systematic reviews have provided solid foundational insights, none have applied a guiding theoretical framework to categorize research, leaving it unclear whether current literature primarily emphasizes social or technical factors. This gap is significant, as sociotechnical systems (STS) theory in related disciplines highlights the need to balance technological advancements with social dynamics for successful implementation. This systematic review applies STS theory to categorize HRA literature, introducing a dual-coding framework across four themes already identified in current reviews, and six dimensions within STS theory. A review of 165 papers reveals a predominant focus on technical rather than social dimensions, indicating an imbalance in themes explored in the existing literature. While technological innovation enhances efficiency, neglecting social factors, such as trust, ethics, and cultural alignment, may limit HRA’s long-term impact. This systematic review advocates for integrating STS theory with future HRA research to promote interdisciplinary approaches that better balance technical and social considerations, and to provide organizations with considerations to foster data-driven cultures and embed ethical governance into AI-driven HR processes, advancing the sustainable implementation of HRA.
