Algorithms in Employment Matchmaking: bridging Public and Private Sectors Approaches

Titolo Rivista STUDI ORGANIZZATIVI
Autori/Curatori Gianluca Scarano, Sofia Rigolli
Anno di pubblicazione 2026 Fascicolo 2026/1
Lingua Inglese Numero pagine 25 P. 9-33 Dimensione file 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.

Il contributo si concentra sulle tecnologie algoritmiche per l’incontro tra domanda e offerta di lavoro adottate dai servizi pubblici per l’impiego e dai dipartimenti di risorse umane aziendali, chiedendosi se compiti funzionali analoghi possano condurre a una convergenza tra i due settori. L’analisi combina una revisione interdisciplinare non sistematica della letteratura con un confronto qualitativo tra due casi selezionati per il loro valore informativo: il servizio pubblico per l’impiego delle Fiandre, da un lato, e una multinazionale dei beni di consumo, dall’altro. Le evidenze sono organizzate attorno a tre dimensioni analitiche - efficienza, agenzia e legittimità - che mettono in luce sia traiettorie condivise sia divergenze strutturali. Entrambi i settori utilizzano algoritmi per standardizzare la valutazione dei candidati e migliorare l’efficienza dei processi di selezione, favorendo una convergenza nelle architetture tecniche e nelle routine operative. Tuttavia, mentre le priorità dei servizi pubblici per l’impiego si concentrano principalmente sull’allocazione delle risorse pubbliche e sulla responsabilità istituzionale, le aziende considerano l’automazione come un vantaggio strategico nell’acquisizione dei talenti. Permangono inoltre divergenze più profonde nelle strutture valoriali di riferimento dei due settori, nelle rispettive soglie di legittimità, nonché nei fattori di agenzia relativi alle professionalità coinvolte. Evidenziando i confini tra i due ambiti, i risultati contribuiscono a problematizzare le narrazioni sulla trasferibilità intersettoriale dell’innovazione algoritmica nella governance dell’occupazione.

Parole chiave:disoccupazione, digitalizzazione, gestione delle risorse umane, mercato del lavoro, intelligenza artificiale, servizi pubblici per l’impiego.

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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