Workplace injury prevention in construction: A data-driven evaluation using WIPAS

Abstract

The construction industry continues to experience persistent workplace injuries despite increasing digitalisation efforts, highlighting the need for evidence-based approaches to safety analytics. This paper investigates how data-driven practices contribute to workplace injury prevention in South African construction and validates a context-specific instrument for assessing such practices. A quantitative survey of construction professionals from firms operating across three provinces was analysed using SPSS and confirmatory factor analysis (CFA) to validate the Workplace Injury Prevention Analytics Scale (WIPAS), alongside descriptive and reliability statistics. Confirmatory factor analysis demonstrated high reliability (Cronbach’s α = 0.937; ρ = 0.940) and acceptable model fit for the newly developed Workplace Injury Prevention Analytics Scale (WIPAS). Within the WIPAS measurement model, items related to predictive analytics and real-time monitoring exhibited the strongest factor loadings, indicating that these advanced capabilities are central indicators of analytics-driven injury prevention maturity in the sampled organisations. Descriptive results indicate that while organisations routinely collect and evaluate safety-related data, advanced capabilities, particularly predictive analytics and real-time monitoring, are inconsistently adopted. These findings highlight a maturity gap between foundational and advanced analytical practices and demonstrate the need for stronger managerial support, systematic employee training, and integrated data governance. This study indicates that, in the surveyed regional cluster of South African construction organisations, firms are already collecting and reviewing safety data but have not yet fully transitioned to advanced, predictive uses of analytics.