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Dominic Cooper PhD is an independent researcher who has authored many books, articles and scientific research papers on safety culture, behavioural-safety and leadership.
May 22, 2024

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Understanding performance variability in safety management: Focusing on the rights and wrongs

For this month’s blog, Dom Cooper looks at safety management and discusses whether health and safety professionals should pay more focus on ‘what went right’, as opposed to ‘what went wrong’ in the workplace.


More and more, the OSH profession is being urged to adopt a focus on what normally goes right, as well as what has gone wrong (incident/accident)[i]. The objective is to understand a system’s performance variability and learn from things that go right, rather than only why they sometimes go wrong[ii].

workplace safetyThere are caveats to this approach that tend to be overlooked in any discussion.

First, focusing on what goes right takes much longer to investigate than focusing on what goes wrong[iii]. Second, investigating what goes right also presupposes that people have the corresponding knowledge of what goes wrong[iv]. Third, a review of studies promoting this approach presents no evidence it improves performance per se[v], or safety performance in particular[vi].

A major articulated reason for adopting this approach is that it addresses perceived complexity in work systems, from which adverse incidents or accidents are thought to emerge. However, both emergence and complexity theories are conceptually underspecified and/or unfalsifiable[vii].

Unfalsifiable theories tell us nothing because they’re true no matter what, which means we cannot use these theories to explain or predict, as they’re compatible with any explanation and any prediction. From a safety management perspective, these attendant prediction and control difficulties[viii] are bad news. The difficulties become self-evident when the issue is looked at from a practical perspective:

  • identifying what is going right, requires reliable, valid data-collection, which could involve extensive observations, interviews, surveys, document analyses etc.;
  • for every task or process under scrutiny, focusing on what goes right is predicated upon accurate and full data reporting of near-misses, human error types, associated behaviours and systems impinging upon the tasks. Self-reporting of such could be problematic, particularly in workplaces where there is a culture of fear;
  • identifying causality between the various practices, systems, behaviours, errors etc., is challenging, to say the least. Many proximal and distal situational and contextual factors and their interrelationships could be in play for every task or process being scrutinised;
  • any findings may be context-specific and not generalisable to other departments, business units etc.
  • the resources involved could perhaps be more usefully deployed elsewhere, especially in times of economic uncertainty.

Examining what goes right tends to include the Functional Resonance Analysis Method (FRAM)i. In principle, users identify and describe essential system functions and their potential variability, define how variability within a function impacts the process, and recommend actions for managing any undesired outcomes. A major problem is that current FRAM-related tools capture neither qualitative and quantitative characteristics of variability nor temporal variations; this means that subjective judgement is used[ix], leading to the axiom ‘garbage in, garbage out’ being in play.

It would seem all FRAM analyses, similar to that of the aviation study example[x], identify a process encompassing a variety of task or process-related behaviours. This suggests that FRAM views ‘behaviour(s)’ as a system component, and attempts to identify its influence on the wider system or process.

In terms of safety management, this is both interesting and important, given that Organisational Behaviour Management (OBM) is a proven process for addressing behaviours in a wide range of settings[xi], and that a major part of human error relates to behavioural choices[xii].

Figure 1: FRAM Analyses for a Cabin Crew’s Pre-Take-off Process

A hexagon represents a function, with the six aspects of input (I), output (O), preconditions (P), resources (R), control (C) and time (T):

Organisational Behaviour Management (OBM)

OBM has been around for 4 decades or so and has been successfully applied to occupational/industrial safety, quality performance, productivity improvement, absenteeism, sales, preventative maintenance and patient infection control[xiii].

OBM essentially seeks to functionally analyse the impact of work systems on behaviour, with the intent of addressing and improving both. It emphasises identifying root causes or system issues that may be contributing to unwanted employee behaviours or outcomes. The goal is not just to reduce problematic behaviours on the surface level, but to uncover and modify systems, structures and policies that may be perpetuating unwanted issues.

Desired outcomes include improving systemic elements (policies, processes, working conditions) as well as associated employee behaviours and performance outcomes through planned and structured interventions. Targeting both systems and behaviours for change, functional assessments consider a number of areas: systems, behavioural precursors, competencies, task resources (e.g., equipment and processes) and outcomes[xiv].

Figure 2: Functional Assessment Targets

The key features that distinguish OBM from other types of managerial interventions are its:

(1) focus on current determinants of behaviour, not prior history. The baggage of the past is abandoned; people are enabled to start afresh.

(2) emphasis on overt behaviour change as one of the criteria for treatment evaluation. OBM evaluates that desired behaviour change was the result of changing the targeted antecedents and that the desired behavioural changes improved the targeted performance outcome(s).

(3) careful targeting of critical behaviours. OBM restricts the foci to the relatively small number of task behaviours responsible for the lion’s share of the performance outcomes. Targeting OSH behaviours known to be associated with near-misses and injuries gives a greater degree of measurement precision and operational control than hitherto.

(4) emphasis on measuring behaviours and monitoring their outcomes. OBM studies use a percentage score[xv] to measure behaviours, which is derived from the ratio between desired (what goes right) and undesired (what goes wrong) behaviours obtained via direct peer-to-peer observations and conversations in the workplace. These simultaneously reinforce desired behavioural norms and improve psychological safety. Observation & conversation data is collated and analysed (usually in dedicated software) to provide detailed weekly feedback to workgroups that facilitates adjustments in behavioural performance and identifies issues they can help resolve.

The derived percentage score also reveals the actual variability in the targeted behaviours over a period of time; subjective guesswork is eliminated. This can be seen in the chart data from an Intensive Care Unit in the North of England where an OBM intervention was designed to reduce or eliminate methicillin-resistant Staphylococcus aureus (MRSA)xiii.

Chart 1: Behavioural Change (Monitoring Variability)

Chart 2: MRSA Reduction Outcomes

(5) emphasis on the involvement of all staff in its development and application. The engagement and participation of both employees and line managers is crucial for success. A genuine partnership between them is ideal as line managers are the guardians of resources, while those on the front line ensure the creation of the product or service. The provision of regular feedback on progress to executives, senior managers and line management is also crucial as it helps to keep them informed, involved and committed to success.


Genuine complex systems[xvi] are difficult to control and any emergences from them (e.g. accidents) are difficult to predict.

A recommended solution is to focus on ‘what goes right’ to facilitate learning, rather than only focusing on things when they go wrong. Methodologies, such as FRAM, designed to map complexity can clearly deconstruct a task or process but don’t possess the means to identify variability as purported, nor does it provide the means on how to move from describing variability to monitoring and controlling it effectively[xvii].

OBM has demonstrably done all these things for decades: [1] It deconstructs tasks and/or processes to determine the impact of various antecedents on the behaviour of those undertaking the tasks/processes; [2] Its measurement process simultaneously focuses on what goes right and/or what goes wrong and, therefore, naturally straddles both perspectives; [3] It measures the variability of task or process behaviours to paint a reasonably accurate picture of variability within a process or task across shifts, workgroups, business units etc., without the guesswork.

This can be done for individual behaviours or a collection of behaviours within a given topic to assist learning. In sum, as numerous case studies show, OBM provides a practical, realistic and proven way forward to achieve everyone’s desired goal of preventing adverse events before they occur.


[i] Hollnagel, E. (2017). Safety-II in Practice: Developing the Resilience Potentials. Taylor & Francis.

[ii] Righi, A. W., Saurin, T. A., & Wachs, P. (2015). A systematic literature review of resilience engineering: Research areas and a research agenda proposal. Reliability Engineering & System Safety141, 142-152.

[iii] Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault tree handbook. Washington, DC: Systems and Reliability Research, Office of Nuclear Regulatory Research, US Nuclear Regulatory Commission.

[iv] Leveson, N. (2020). Safety 3: A systems approach to safety & resilience. White Paper.

[v] Patriarca, R., Bergström, J., Di Gravio, G., & Costantino, F. (2018). Resilience engineering: Current status of the research and future challenges. Safety Science, 102.

[vi] Pillay, M. (2018). Resilience engineering: An integrative review of fundamental concepts and directions for future research in safety management. Open Journal of Safety Science and Technology7(4), 129-160.

[vii] Baranger, M. (2001). Chaos, complexity, and entropy: A physics talk for non-physicists. MIT Press.

[viii] Thurner, S., Hanel, R., & Klimek, P. (2018). Introduction to the Theory of Complex Systems. Oxford University Press.

[ix] Salehi, V., Smith, D., Veitch, B., & Hanson, N. (2021). A dynamic version of the FRAM for capturing variability in complex operations. MethodsX8, 101333.

[x] Tian, W. & Caponecchia, C. (2020). Using the functional resonance analysis method (FRAM) in aviation safety: A systematic review. Journal of Advanced Transportation2020, 1-14.

[xi] Stajkovic, A. D. & Luthans, F. (1997). A meta-analysis of the effects of organizational behaviour modification on task performance, 1975–95. Academy of Management Journal40(5), 1122-1149.

[xii] Cooper, M. D. & Finley, L. J. (2013). Strategic Safety Culture Roadmap, BSMS Inc. Franklin, IN, USA.

[xiii] Cooper, D., Farmery, K., Johnson, M., Harper, C., Clarke, F. L., Holton, P., Wilson, S., Rayson, P., & Bence, H., (2005). Changing personnel behavior to promote quality care practices in an intensive care unit. Therapeutics and Clinical Risk Management1(4), 321-332.

[xiv] Gravina, N., Nastasi, J., & Austin, J. (2021). Assessment of employee performance. Journal of Organizational Behavior Management41(2), 124-149.

[xv] Cooper, M. D. (2009). Behavioral safety interventions: A review of process design factors. Professional Safety54(02).

[xvi] Cooper, M.D. (2024). Rule-6 Tames Complexity. March.

[xvii] Verhagen, M. J., de Vos, M. S., Sujan, M., & Hamming, J. F. (2022). The problem with making Safety-II work in healthcare. BMJ Quality & Safety31(5), 402-408.

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Robin Phillips
Robin Phillips
28 days ago

As always references and evidence to support