An ESCO-Based Skill-Gap Detection Framework for SMEs: A Design Science Prototype of an Intelligent Learning Management System

Angelo Leogrande, Mauro Di Molfetta (), Nicola Magaletti (), Valeria Notarnicola () and Maria Giovanna Trotta ()
Additional contact information
Nicola Magaletti: LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro
Valeria Notarnicola: LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro

Working Papers from HAL

Abstract: The growing misalignment between workforce competences and the skill requirements of digitally evolving occupations is a critical barrier to SME competitiveness in the Industry 4.0 and 5.0 transitions. This study develops and demonstrates, through a working prototype, an ESCOaligned analytical framework for skill-gap detection and adaptive reskilling, produced within the LUCE (LUtech Campus Ecosystem) project. Its contribution is theoretical, methodological, and technological rather than purely applicative. Drawing on human capital theory, the knowledge-based view of the firm, and skill-biased technical change, it conceptualizes reskilling as the relaxation of a firm-level humancapital-technology complementarity constraint, using ESCO to render this constraint observable and commensurable across firms. Methodologically, it defines the skill gap as a standardized, ontology-grounded measure; technologically, it integrates functions usually kept separateperformance analytics, skill assessment, and learning provisioninto a single pipeline, instantiated as a proof-of-concept Intelligent Learning Management System. Workforce competences are extracted from anonymized employee CVs via a deterministic, rulebased Natural Language Processing procedure, mapped to ESCO preferred labels, alternative labels, and concept URIs, and linked to occupational requirements through occupation-skill relations. A Skill Gap Indicator, defined as the complement of evidenced ESCO competence coverage, yields a mean of 0.956, interpreted as a conservative upper-bound estimate rather than a literal measure of absent competences. The prototype then translates detected gaps into targeted training recommendations. As a research prototype, the system is validated along its technical and analytical dimensions rather than for training effectiveness; the relationship between gap closure and firm performance is advanced as a proposition for future longitudinal evaluation.

Keywords: Natural Language Processing. 1. Introduction; Intelligent Learning Management System; People Analytics; Human Capital; SMEs; Digital Transformation; Workforce Reskilling; Skill Gap Analysis; ESCO; ESCO Skill Gap Analysis Workforce Reskilling Digital Transformation SMEs Human Capital People Analytics Intelligent Learning Management System Natural Language Processing. 1. Introduction (search for similar items in EconPapers)
Date: 2026-06-25
Note: View the original document on HAL open archive server: https://hal.science/hal-05669981v1
References: Add references at CitEc
Citations:

Downloads: (external link)
https://hal.science/hal-05669981v1/document (application/pdf)

Related works:
Working Paper: An ESCO-Based Skill-Gap Detection Framework for SMEs: A Design Science Prototype of an Intelligent Learning Management System (2026)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-05669981

Access Statistics for this paper

More papers in Working Papers from HAL
Bibliographic data for series maintained by CCSD ().