Safety Science is an interdisciplinary field that encompasses the study of safety across human activities, integrating perspectives from social sciences, engineering, natural sciences, and medicine (Hollnagel, 2014). This paper compiles key safety theories, drawing from foundational and contemporary frameworks that have shaped safety management and research. These theories address human behavior, organizational systems, and risk management, offering insights into achieving safer workplaces and systems. Below, we explore prominent safety theories, their principles, and their applications, with a focus on Safety-I, Safety-II, the Swiss Cheese Model, High Reliability Organizations (HRO), Normal Accident Theory (NAT), Human Factors Analysis and Classification System (HFACS), and the 24Model.
Safety-I and Safety-II
Safety-I defines safety as the absence of adverse outcomes, focusing on minimizing accidents and incidents through reactive risk management (Hollnagel et al., 2015). This approach measures safety by counting failures, emphasizing what goes wrong and addressing risks reactively (Sujan et al., 2019). For example, Safety-I seeks to reduce accident rates through policies and procedures that control risks, often isolating system components for analysis (Dekker, 2019). Critics argue that this reductionist approach overlooks the interconnected nature of complex systems, making it less effective in modern, dynamic environments (Woolley et al., 2019).
In contrast, Safety-II shifts the focus to ensuring successful outcomes under varying conditions, emphasizing proactive management and learning from what goes right (Hollnagel et al., 2015). Safety-II measures safety through successes, promoting resilience and adaptability in systems (Patterson & Deutsch, 2015). It encourages decentralized decision-making, lean management, and understanding trade-offs in daily operations (Dekker, 2019). Safety-II advocates argue that Safety-I’s bureaucratic control and reactive stance are outdated, while Safety-I proponents criticize Safety-II for lacking empirical evidence and clear metrics for measuring positives (Cooper, 2020; Wang et al., 2020). Both approaches aim for safety but differ in philosophy and methodology, with Safety-II emphasizing holistic system interactions over isolated component analysis (Jones et al., 2018).
Swiss Cheese Model
The Swiss Cheese Model, developed by James T. Reason, is a widely used risk management framework that illustrates how accidents occur when multiple layers of defenses fail (Reason, 1990a). Each layer of defense, analogous to a slice of Swiss cheese, contains holes (latent and active failures) that, when aligned, allow risks to penetrate the system, leading to accidents. For instance, in the context of the COVID-19 pandemic, individual measures like masks or social distancing have limitations, but multiple overlapping measures increase the likelihood of stopping the virus (Reason, 1990a). Originally developed to explain traffic accidents, the model has been applied in aviation, healthcare, and cybersecurity, highlighting the need for layered defenses to mitigate risks (Shappell & Wiegmann, 2000). However, critics note its linear and reductionist nature, which may not fully capture the dynamic interactions in complex systems (Dekker & Leveson, 2014).
High Reliability Organizations (HRO) Theory
High Reliability Organizations (HROs) operate in high-risk environments, such as nuclear power plants and air traffic control, yet maintain exceptionally low accident rates (Roberts & Rousseau, 1989). HRO theory identifies five key characteristics: preoccupation with failure, reluctance to simplify, sensitivity to operations, commitment to resilience, and deference to expertise (Weick et al., 1999). These principles foster a collective mindfulness culture, where organizations anticipate risks, learn from weak signals, and adapt to unexpected events (Weick & Sutcliffe, 2001). Unlike Normal Accident Theory (NAT), which posits that accidents are inevitable in complex systems (Perrow, 1984), HRO theory suggests that strategic organizational practices can prevent catastrophic failures (Badia et al., 2020). HROs prioritize decentralized decision-making during crises and rigorous evaluation of changes to avoid oversimplification (Weick & Sutcliffe, 2007).
Normal Accident Theory (NAT)
Normal Accident Theory (NAT), proposed by Charles Perrow, argues that accidents are inevitable in tightly coupled and complex systems, such as nuclear power plants or petrochemical facilities (Perrow, 1984). These “normal” accidents arise from unpredictable interactions among system components, making complete prevention impossible. NAT contrasts with HRO theory by emphasizing the inherent fallibility of complex systems, even with robust safety measures (Perrow, 1984). While HROs focus on organizational strategies to achieve reliability, NAT highlights the limitations of such efforts in highly complex environments (Shrivastava et al., 2009). NAT has been influential in understanding systemic risks in industries like aviation and energy, underscoring the need for comprehensive risk management frameworks (Tamuz & Harrison, 2006).
Human Factors Analysis and Classification System (HFACS)
The Human Factors Analysis and Classification System (HFACS), built on Reason’s Swiss Cheese Model, provides a structured framework for analyzing accidents by categorizing failures into four levels: unsafe acts, preconditions for unsafe acts, unsafe supervision, and organizational influences (Shappell & Wiegmann, 2000). HFACS distinguishes between active failures (e.g., errors by frontline workers) and latent failures (e.g., management deficiencies), enabling systematic identification of causal factors across industries like aviation, rail, and healthcare (Hulme et al., 2019b). By analyzing historical data, HFACS helps organizations identify recurring trends and implement targeted interventions to enhance human performance and reduce accident rates (Wiegmann & Shappell, 2001). Its strength lies in its ability to link individual actions to organizational factors, though it may lack detailed specifications for certain management-related causes (Punzet et al., 2018).
24Model
The 24Model is a systemic accident causality model developed in China, integrating elements from the Domino Theory, Swiss Cheese Model, and Loss Causality Model (Fu et al., 2020). It categorizes accident causes into two levels (organizational and individual) and four stages (safety culture, safety management system, individual safety capacity, and safety actions/conditions). The model emphasizes detailed causal factor definitions, enabling precise accident analysis and prevention strategies (Chen & Li, 2017). Its systemic approach accounts for nonlinear interactions in complex systems, supporting applications in accident analysis, safety management, and safety culture development (Fu et al., 2022). The 24Model’s formal structure facilitates computer-based accident modeling and has been refined through multiple iterations to enhance its applicability across industries (Xu et al., 2021).
Conclusion
Safety Science integrates diverse theories to address safety challenges in complex systems. Safety-I and Safety-II offer contrasting paradigms, with Safety-I focusing on failure prevention and Safety-II emphasizing success and resilience. The Swiss Cheese Model underscores the importance of layered defenses, while HRO theory highlights organizational strategies for reliability. NAT acknowledges the inevitability of accidents in complex systems, and HFACS provides a structured approach to analyzing human and organizational failures. The 24Model offers a systemic framework for understanding accident causality. Together, these theories provide a robust foundation for advancing safety management, with applications across industries like aviation, healthcare, and energy. Future research should bridge theoretical and practical gaps to enhance safety outcomes in increasingly complex environments.
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