The study addresses the growing demand for evidence-based methods to assess the effectiveness of project managers in engineering-intensive industries. An exploratory competency model was developed in a German automotive R&D service division, integrating both behavioral and technical dimensions into a quantifiable measure termed the competency coefficient (K). Ten project managers, classified as either superior or average performers, were evaluated across 20 individual competencies. Behavioral Event Interview (BEI) results revealed substantial differences between the two groups, with effect sizes exceeding d = 2.0 for competencies such as concern for order, customer orientation, and impact and influence, while core cognitive competencies Analytical thinking (AT), conceptual thinking (CT), information seeking (INFO) demonstrated significant but more variable effects (d = 1.2–2.0). Expert panel evaluations (n = 33) showed strong consensus (Kendall’s W = 0.59, p <.001), with average relevance scores above 80%. Sensitivity analyses of alternative weighting schemes for the K metric (e.g., 3–2–1; 4–2–1; 5–3–1) yielded highly stable rankings (Spearman’s ρ ≥ 0.97), indicating robustness of the results. Initial validation against project performance indicators suggested significant positive correlations (r = 0.65–0.88, p <.01) between K values and Key Performance Indicators (KPIs). Within the examined automotive R&D division, the findings suggest that individual competencies—particularly cognitive and INFO skills—were the most distinctive differentiators of high-performing project managers, whereas technical expertise alone did not explain performance differences. The competency coefficient provides a structured, quantifiable framework for linking competencies to project outcomes, though further validation across broader datasets and organizational contexts remains necessary.

Purpose: This paper aims to evaluate the predictive validity of the Competency Coefficient (K), a behavioral indicator derived from structured assessments of automotive R&D project managers, by examining its correlation with objective project performance outcomes. Methodology: The study performs a statistical analysis of K against five z-standardized Key Performance Indicators (KPIs) to identify the relationship between behavioral competencies and project performance. Predictive validity was evaluated using Pearson/Spearman correlations and Ordinary Least Squares (OLS) regression; robustness in small samples and model adequacy were assessed with 10,000-sample bootstrap intervals, Leave-One-Out Cross-Validation (LOOCV),Prediction Sum of Squares (PRESS) (PRESS/Q²), and tests for quadratic nonlinearity. Findings: Results reveal positive and statistically significant associations between the Competency Coefficient (K) and KPI-based performance indices. The linear model explained roughly 93% of the variance in project results, and cross-validation confirmed consistent out-of-sample performance (Q² = 0.88). The restricted sample size (n = 7) and singular organizational environment limit generalizability, while contextual factors may also influence the reported outcomes. Originality/Value: The paper provides original empirical evidence that competency-based behavioral indicators can function as dependable, measurable elements of project performance assessment. The findings emphasize methodological feasibility rather than universal applicability. The contribution lies in the measurement and validation technique, which may be duplicated for verification in larger and more diverse samples.