Identify and classify common errors, antecedents, outcomes, and mitigation strategies in qualitative and semi-quantitative workplace safety risk management: Integrating grounded theory and systematic literature review

• Published in: Safety science Vol. 187; p. 106851
• Main Authors: Rajabi, Fazel, Jahangiri, Mehdi, Alimohammadlou, Moslem, Kamalinia, Mojtaba
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2025
• Subjects: Errors and pitfalls; Grounded theory; Mitigation strategies; Systematic literature review; Workplace safety risk management (WSRM); Errors and pitfalls; Grounded theory; Workplace safety risk management (WSRM); Systematic literature review; Mitigation strategies
• ISSN: 0925-7535
• DOI: 10.1016/j.ssci.2025.106851

•WSRM is vital for safety; but errors can undermine its effectiveness.•A mixed-methods approach was utilized to identify and classify common errors and pitfalls in WSRM.•Research found 49 errors in WSRM, along with 70 influencing factors.•The study offer valuable insights for developing evaluation tools and error mitigation strategies WSRM processes. Risk management is a crucial tool for ensuring workplace safety. However, errors in this process can significantly undermine its effectiveness. This study aims to identify and classify common errors and pitfalls, along with their antecedents, outcomes, and mitigation strategies, within the qualitative and semi-quantitative Workplace Safety Risk Management (WSRM) processes. This research utilized a mixed-methods approach, integrating systematic review methodologies with grounded theory. A descriptive systematic review synthesized evidence from existing literature, utilizing databases such as Web of Science, PubMed, and Scopus. Additionally, empirical data were gathered through semi-structured interviews with ten subject matter experts. Data analysis was performed using the grounded theory approach, supported by MAXQDA software. A total of 49 distinct errors and pitfalls in WSRM were identified and categorized into seven classifications. Furthermore, 70 open codes related to influencing factors were identified, which included antecedent conditions, intervening factors, mitigation strategies, and outcomes, organized into relevant subcategories. The findings of this study enhance our understanding of the factors that contribute to errors in qualitative and semi-quantitative WSRM. This knowledge can serve as a foundation for developing evaluation tools and error mitigation strategies within WSRM processes.