Systematic literature review on the digital transformation of the personnel selection process

Virág Baranyi

German Journal of Human Resource Management2025https://doi.org/10.1177/23970022251363012article
AJG 2ABDC B
Weight
0.50

Abstract

Digital Transformation technologies (DT technologies) are reshaping work processes, including personnel selection, an area traditionally viewed as inherently human-centric. While prior studies have examined various digital technologies in personnel selection, they have not provided sufficient evidence on the different levels of digitalization in selection processes and the factors influencing organizations’ adoption decisions. To address these gaps, this study systematically reviews 94 Scopus-indexed studies to analyze how DT technologies are applied across selection stages, categorizing practices into Manual, Digitalized, and Digitally Transformed approaches. By further distinguishing between Digital Technologies and AI Enhancements, this study offers a structured framework for understanding how organizations integrate digital technologies into selection and what drives or hinders their adoption. The findings highlight both the benefits (efficiency gains, potential bias reduction, improved candidate experience) and challenges (ethical concerns, algorithmic bias, technical and cultural barriers, and candidate perceptions) associated with these technologies, providing insights for both academic research and HR practice.

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https://doi.org/https://doi.org/10.1177/23970022251363012

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@article{virág2025,
  title        = {{Systematic literature review on the digital transformation of the personnel selection process}},
  author       = {Virág Baranyi},
  journal      = {German Journal of Human Resource Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/23970022251363012},
}

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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.