SME triumph in the European Union: Blending digital, green, hard, and soft skills

Sofia Gomes et al.

Journal of the International Council for Small Business2026https://doi.org/10.1080/26437015.2026.2637729article
ABDC C
Weight
0.50

What the paper says

This study maps how European small and medium-sized enterprises (SMEs) value worker skills (digital, green, hard, and soft) and links these priorities to firm characteristics. Using survey data from 11,474 SMEs across the 27 European Union countries, we estimate a multilevel latent class model to uncover distinct patterns of skill importance and to capture heterogeneity within and between countries. Results reveal several skill‑priority profiles, differing in the relative weight placed on digital and green competences versus hard and soft skills. Firm size, age, turnover, and sector significantly predict membership in these profiles, indicating that skill strategies vary systematically with organizational resources and market context. By showing how SMEs selectively develop capabilities to respond to changing technological and sustainability demands, the findings extend dynamic capabilities theory with comparative evidence. The study provides a harmonized cross‑country map of SME skill priorities and documents both intra‑ and international variation in the European business landscape for policy and managerial decision making.

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https://doi.org/https://doi.org/10.1080/26437015.2026.2637729

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@article{sofia2026,
  title        = {{SME triumph in the European Union: Blending digital, green, hard, and soft skills}},
  author       = {Sofia Gomes et al.},
  journal      = {Journal of the International Council for Small Business},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/26437015.2026.2637729},
}

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SME triumph in the European Union: Blending digital, green, hard, and soft skills

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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.