Development and validation of miner career sustainability scale (MCSS) in intelligent coal mines: a network analysis...

Career sustainability is essential for maintaining employability, health, and well-being in dynamic work environments. Existing measures, developed primarily for general or traditional manufacturing settings, may not adequately capture the unique challenges facing frontline miners in intelligent coal mines, where technological disruption intersects with high‑risk physical environments. This study evaluated the psychometric properties of the newly developed Miner Career Sustainability Scale (MCSS) and complemented traditional factor analysis with network analysis to examine the construct's structural organization. Based on pilot (N = 696) and formal (N = 3864) samples from five state‑owned intelligent coal mines in Shanxi Province, China, confirmatory factor analysis (CFA) provided overall support for a second‑order seven‑factor model (CFI = 0.976, TLI = 0.972, RMSEA = 0.067, SRMR = 0.019). A bifactor model revealed a dominant general factor (ECV = 0.904) with meaningful specific dimensions (ωH = 0.579). Full measurement invariance was established across younger (< 45 years) and older (≥ 45 years) miners. Network analysis, employed as a complementary exploratory tool rather than a confirmatory one, identified five empirical communities. Core nodes included understanding of intelligent systems (CS2), psychological pressure management (CS11), and experience integration (CS17), while continuous learning willingness (CS25) showed the highest bridge strength, suggesting its role as a structural hub. Age‑group networks were largely invariant, though older miners had higher centrality for human-machine tacit understanding (CS8). The scale captures context‑specific dimensions such as human-robot collaboration adaptation and experience integration, extending the career sustainability framework to technology‑disruptive, high‑risk contexts. Criterion validity was supported by high correlations with safety performance (r = 0.844) and job satisfaction (r = 0.832). These findings provide preliminary evidence for the MCSS as a psychometrically sound instrument and demonstrate the value of integrating network analysis with traditional factor analysis in scale development research.