Unlocking the power of human capital: how SMEs face risks

Mariia Molodchik et al.

Journal of Economic Studies2026https://doi.org/10.1108/jes-11-2025-0878article
AJG 2ABDC B
Weight
0.50

Abstract

Purpose This study investigates how human capital (HC) efficiency influences risk-taking behavior among small- and medium-sized enterprises (SMEs), particularly under varying contextual conditions such as economic crises and firm size. Design/methodology/approach Using a novel industry-adjusted metric, Abnormal Return on Human Capital, the article analyzes a large panel dataset of 154,994 Russian SMEs spanning 2013–2023. Risk is measured via the volatility of return on sales (ROS). The analysis employs fixed-effects regression models with interaction terms to capture the moderating roles of firm size and external shocks. Findings The results indicate that higher HC efficiency is positively associated with greater risk-taking in SMEs. However, this relationship is moderated by contextual factors. Specifically, efficient HC reduces risk exposure during moderate crises and is more influential in smaller firms than in larger ones. Originality/value The study contributes by integrating HC analysis into firm-level risk research through a novel, competitively benchmarked metric. It extends contingency theory and the dynamic capability theory by showing that HC's impact on risk is contingent upon external and internal conditions, offering theoretical and practical insights into SME risk navigating.

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https://doi.org/https://doi.org/10.1108/jes-11-2025-0878

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@article{mariia2026,
  title        = {{Unlocking the power of human capital: how SMEs face risks}},
  author       = {Mariia Molodchik et al.},
  journal      = {Journal of Economic Studies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/jes-11-2025-0878},
}

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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

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