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Profiling employability skills for the digital and circular transition in the agri-food sector: a big data analysis

Francesco Smaldone et al.

British Food Journal2026https://doi.org/10.1108/bfj-04-2025-0525article
AJG 1ABDC B
Weight
0.37

Abstract

Purpose This study aims to systematically map the employability skills the agri-food sector (AFS) requires to support its twin transition (TT) towards digitalisation and circularity. Grounded in employability theories, it proposes a dual-dimensional framework that classifies these skills into thematic clusters and cognitive topics, offering strategic insights for workforce development and policy design. Design/methodology/approach The study adopts a big-data-driven approach leveraging Large Language Models and Transformers. Text mining and topic modelling techniques were applied to a large corpus of job advertisements collected via web scraping from major employment platforms, specifically focusing on the European agri-food labour market. Findings The analysis generated a taxonomy of employability skills articulated along two dimensions: semantic topics (hard, soft, and transversal skills) and cognitive clusters (industry-specific, circular, and digital). Industry-specific skills prevail in the early stages of the supply chain, digital skills peak in logistics and traceability, while circular skills are concentrated in upstream and downstream phases. Hard skills dominate technical roles, soft skills emerge in customer-facing functions, and transversal skills are central to end-of-life stages. Originality/value This is the first study profiling employability skills for the TT in the AFS using a large-scale, data-intensive methodology. Integrating employability theories with empirical labour market signals, this study develops an original taxonomy aligning sectorial transformation with individual career readiness.

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https://doi.org/https://doi.org/10.1108/bfj-04-2025-0525

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@article{francesco2026,
  title        = {{Profiling employability skills for the digital and circular transition in the agri-food sector: a big data analysis}},
  author       = {Francesco Smaldone et al.},
  journal      = {British Food Journal},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/bfj-04-2025-0525},
}

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Profiling employability skills for the digital and circular transition in the agri-food sector: a big data analysis

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
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.