A systemic approach to accident prevention: How control factors influence accident severity and losses across industries.

Accidents are often attributed to frontline operator errors, overshadowing higher-level organizational and regulatory factors. This study integrates Systems-Theoretic Accident Model and Processes (STAMP) with fuzzy-set Qualitative Comparative Analysis (fsQCA) and Necessary Condition Analysis (NCA) - a configurational approach - to examine 80 major accident investigation reports from five high-risk Chinese industries (chemical, construction, transportation, coal mining, firefighting) spanning 2010-2022. Four systemic control elements (control activities errors, feedback errors, controller failures, controlled process errors) were assessed against three severity indicators (fatalities, injuries, direct economic losses). Results reveal distinct yet overlapping causal pathways. In chemical accidents, feedback errors are crucial for high fatalities. Construction and coal mining often link early controller/control activity failures to severe outcomes. Transportation highlights control activity errors for injuries, while firefighting points to the combination of control activity errors and controller failures. NCA corroborates key factors like feedback errors and controller failures as necessary conditions (effect sizes d > 0.1, p < 0.05). While supplementary statistical analysis confirmed these factors' general importance, it faced data limitations (small N, collinearity); the fsQCA/NCA approach provided more robust insights into combinatorial pathways and necessity. Bottleneck analyses further indicate that even modest increments in key errors can trigger disproportionately large losses. These findings underscore the need for multi-level interventions-strengthening feedback loops, organizational oversight, and control processes-to mitigate accident severity in complex socio-technical systems, demonstrating the utility of configurational methods for understanding systemic failures.