Experienced executives in several emerging European economies are linked with weaker firm performance, while employee training and digital technology adoption boost outcomes; digital adoption partly transmits the gains from training.

Main Finding

Using 4,946 firm‑level observations from emerging European economies, the paper finds that greater managerial experience is associated with lower firm performance (a "cognitive rigidity trap"), while investments in employee training and digital technology adoption raise performance. Digital adoption partially mediates the positive effect of employee training on firm performance. Female managerial presence was also reported to have a negative association with performance in this sample (contrary to the authors' initial hypothesis).

Key Points

• Research question: Do managerial characteristics (especially managerial experience) affect firm performance, and can internal mechanisms (employee training, digital adoption) mitigate any managerial rigidity?
• Main theoretical framing: Upper Echelons Theory (managerial cognition), Resource‑Based View (training as firm resource), and Dynamic Capabilities Theory (digital adoption as reconfiguration capability).
• Main empirical findings:
  • Managerial experience → statistically significant negative effect on firm performance (interpreted as cognitive rigidity/entrenchment).
  • Female managerial presence → negative association with firm performance in this dataset (noted as contrary to many prior studies).
  • Employee training → positive direct effect on firm performance.
  • Digital technology adoption → positive direct effect on firm performance.
  • Employee training → positive effect on digital adoption.
  • Digital adoption significantly mediates the training → performance link.
• Policy / managerial recommendation from authors: Pair workforce training with concrete digital workflows (training‑plus‑digital adoption), and implement executive upskilling to reduce cognitive inertia rather than immediate replacement of senior leaders.
• Limitations noted by authors: cross‑sectional design (no long‑run dynamics, adaptation lags, or path dependence), inference of "cognitive rigidity" from observed correlations rather than direct psychological measurement.

Data & Methods

• Data source: World Bank Enterprise Surveys (World, 2024). Sample: 4,946 firm‑level observations from emerging European economies (Central and Eastern Europe / EU‑accession countries).
• Key variables (operationalized from survey data): firm performance (financial outcomes), managerial experience (tenure/years), female managerial presence (gender composition), employee training (training investment/indicator), digital technology adoption (binary/score for adoption of digital tools).
• Econometric approach:
  • Ordinary Least Squares (OLS) regressions with robust standard errors to estimate direct effects.
  • Generalized Structural Equation Modeling (GSEM) mediation analysis (with robust SEs) to test whether digital adoption mediates the training → performance relationship.
• Robustness & inference:
  • Results reported as statistically significant for the stated directions; the paper interprets the negative experience → performance link as evidence of a cognitive rigidity trap, but causal claims are limited by cross‑sectional design and potential endogeneity.
• Missing from public summary: exact coefficient magnitudes, standard errors, R²s, and specification checks (not provided in the supplied text).

Implications for AI Economics

• Managerial cognition matters for AI adoption and productivity gains:
  • Executive tenure and cognitive rigidity can slow or distort AI/digital technology adoption; empirical models of AI diffusion and productivity should include managerial characteristics (tenure, prior IT experience, cognitive measures) as moderators.
• Training + digital workflows as complementary investments:
  • Employee training raises the returns to digital adoption and therefore to AI investments. Policies and firm strategies that pair workforce reskilling with concrete AI workflow integration will yield larger productivity gains than either alone.
• Mediation pathway relevant for measuring AI returns:
  • The finding that digital adoption mediates training → performance implies that micro‑level AI adoption (use intensity, integration into processes) is a crucial intermediary to measure when estimating the productivity effects of AI investments.
• Heterogeneous diffusion and inequality:
  • Emerging economies (or firms within economies) with entrenched leadership may adopt AI more slowly or extract lower returns, producing cross‑firm and cross‑region heterogeneity in AI benefits — relevant for models of aggregate productivity and labor market impacts.
• Policy design:
  • To accelerate beneficial AI adoption, combine subsidies or incentives for AI tools with funded training programs tied to actual AI workflows and executive upskilling programs focused on digital strategy and governance.
• Empirical research suggestions for AI economics:
  • Use panel data, natural experiments, or instrumenting strategies to identify causal effects of managerial characteristics on AI adoption and productivity.
  • Measure cognitive rigidity or managerial attitudes directly (surveys, psychometrics) to sharpen mechanisms.
  • Disaggregate "digital adoption" to AI/ML usage, automation intensity, and analytics integration to estimate where managerial effects are strongest.
  • Explore interaction effects between firm size, industry, and country digital infrastructure (e.g., broadband, data regulation) on AI returns.
• Macro/aggregate modeling:
  • When projecting the macroeconomic impacts of AI, incorporate low adoption or lower returns among firms with long‑tenured management as a drag on diffusion; targeted executive retraining can be modeled as a policy lever to accelerate aggregate productivity gains.