EHS Analytics for Improving Hazard Communication, Training Effectiveness, and Incident Reporting in Industrial Workplaces

Abstract

This study addresses a persistent problem in industrial enterprises: EHS programs generate substantial data, yet many workplaces struggle to translate those signals into consistently clear hazard communication, effective safety training, and high-quality incident reporting. The purpose was to test whether stronger EHS analytics capability predicts these upstream EHS process outcomes within an enterprise case workplace that uses routine EHS logs, dashboards, and trend reviews. This responds to growing interest in leading indicators and data-driven safety governance. A quantitative, cross-sectional, case-based survey was administered to employees across operational roles. After data-quality screening, 210 valid responses were retained from 228 submissions, with frontline operators representing 52.4%, supervisors 27.6%, and EHS or support staff 20.0%; mean experience was 6.8 years (SD = 4.9). The independent variable was EHS Analytics Capability (EHSAC, 8 items; M = 3.62, SD = 0.67; α = 0.88) and the dependent variables were Hazard Communication Quality (HCQ, M = 3.71, SD = 0.63; α = 0.86), Training Effectiveness (TE, M = 3.68, SD = 0.61; α = 0.89), and Incident Reporting Quality (IRQ, M = 3.55, SD = 0.70; α = 0.87). The analysis plan combined descriptive statistics, reliability testing, Pearson correlations, and ordinary least squares regressions, followed by robustness models controlling for role and experience. EHSAC showed moderate-to-strong correlations with HCQ (r = 0.56), TE (r = 0.52), and IRQ (r = 0.49), all p < .001. Regression results provided convergent evidence, with EHSAC significantly predicting HCQ (β = 0.56, t = 10.42, R² = 0.34), TE (β = 0.52, t = 9.32, R² = 0.29), and IRQ (β = 0.49, t = 8.36, R² = 0.24), and remaining significant with controls (β = 0.51, 0.47, 0.43; adjusted R² = 0.38, 0.32, 0.27). Item diagnostics indicated that timely feedback after reporting (M = 3.21) and post-training reinforcement (M = 3.39) were key gaps. Findings imply that investing in analytics capability and strengthening feedback loops can measurably improve communication, learn transfer, and report quality in enterprise EHS systems.