Effects of leadership, organizational culture, and career development on employee performance: Evidence from the Jabotabek railway infrastructure work unit

Abstract

Human resources drive organizational outcomes, but their impact depends on how leadership, culture, and career systems are designed and enacted. This study examines whether leadership style, organizational culture, and career development jointly and separately predict employee performance in the Jabotabek Railway Infrastructure Work Unit (Indonesia). Using an explanatory, cross-sectional survey of staff across operations, maintenance, and administration, we measured leadership (transformational plus contingent reward), cultural alignment (involvement, consistency/discipline, adaptability, mission), career development (clarity, fairness, mentoring/training access), and performance (in-role execution and discretionary service behaviors). Reliability and assumption checks were satisfactory (α: leadership = 0.930; culture = 0.937; career = 0.946; performance = 0.865; all K–S p > .05). Bivariate OLS showed each predictor was positively associated with performance: leadership (R² = .225), culture (R² = .201), and career development (R² = .231). The multiple regression model indicated complementary effects: leadership (β = 0.200), culture (β = 0.196), and career (β = 0.179) jointly explained 35.8% of variance in performance (R = .598). Findings align with meta-analytic evidence that influence-centric leadership and aligned cultures elevate effectiveness and citizenship behavior, while transparent career systems sustain motivation and retention (Judge & Piccolo, 2004; Denison & Mishra, 1995; Ng et al., 2005). Practically, results justify a balanced, systems approach: (i) shift everyday supervision from command to coaching with clear goals and contingent rewards; (ii) codify a few non-negotiable cultural routines that translate safety, reliability, and respect into behaviors; and (iii) publish a visible internal labor market with competency-based progression and mentoring. These steps should reduce performance variance across shifts/depots and convert “good on average” conditions into consistently excellent passenger experiences. Empirical statistics are drawn from the analyzed file.