Algorithms in Employment Matchmaking: bridging Public and Private Sectors Approaches

Journal title STUDI ORGANIZZATIVI
Author/s Gianluca Scarano, Sofia Rigolli
Publishing Year 2026 Issue 2026/1
Language English Pages 25 P. 9-33 File size 189 KB
DOI 10.3280/SO2026-001001
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This study investigates how public employment services (PES) and corporate human resource departments implement algorithmic technologies in employment matchmaking, asking whether analogous functional tasks lead to convergence in technological practices across sectors. The analysis combines an interdisciplinary non-systematic literature review with a qualitative comparison of two cases of informative value: the Flemish PES and a multinational consumer goods company using AI-driven recruitment tools. The evidence is organised around three analytical dimensions - efficiency, agency, and legitimacy - which illuminate both shared trajectories and structural divergences. Both sectors leverage algorithms to standardise candidate assessment and improve process efficiency, fostering convergence in technical architectures and operational routines. However, these similarities conceal deeper divergences. PES focuses on resource allocation and accountability, while companies view automation as a strategic advantage in talent acquisition. The study shows that convergence occurs at the level of instruments, while divergence persists at the level of value structure, legitimacy thresholds, and forms of professional agency. Insights from this study may inform policy and organisational design in both sectors. By highlighting the boundaries between the two sectors, the evidence nuances narratives of cross-sectoral transferability of algorithmic innovation in employment governance.

Keywords: unemployment, digitalisation, human resource management, labour market, artificial intelligence, public employment services.

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Gianluca Scarano, Sofia Rigolli, Algorithms in Employment Matchmaking: bridging Public and Private Sectors Approaches in "STUDI ORGANIZZATIVI " 1/2026, pp 9-33, DOI: 10.3280/SO2026-001001