Abstract:

Abstract

This qualitative study explores the influence of cognitive biases on the decision-making processes of occupational safety and health (OSH) professionals. Through a thematic analysis of survey responses from 60 professionals, the research identified six recurrent biases: fundamental attribution error, hindsight bias, confirmation bias, negativity bias, illusory correlation, and outcome bias. The findings reveal that these biases foster a tendency to attribute incidents primarily to worker behavior, over-rely on lagging indicators, and oversimplify complex events, while underestimating situational and organizational factors. The study concludes that unaddressed cognitive biases can perpetuate a culture of individual blame and distort risk assessments, and it advocates for a shift toward safety frameworks that recognize human complexity and build more adaptive and resilient systems.

Keywords: Cognitive Biases, Occupational Safety and Health (OSH), Decision-Making, Workplace Safety, Human Factors

Introduction

            In the complex landscape of occupational safety and health (OSH), the human element has long been a focal point of research and practice. From the early days of the industrial revolution to the modern era, understanding how human behavior contributes to workplace safety has been a critical concern for OSH professionals (Dekker, 2019). Traditional safety approaches, rooted in theories such as those of H.W. Heinrich, often emphasize compliance with rules, regulations, and the mitigation of human error (Busch, 2019; Heinrich, 1941). However, these approaches usually attribute unplanned events to individual behavior, rather than acknowledging inherent variability in complex systems, organizational dynamics, or environmental factors.

Cognitive biases, inherent to human decision-making, shape how safety professionals perceive, evaluate, and respond in day-to-day workplace practices (Van Wassenhove et al., 2022). To explore these experiences and perspectives, the author’s engaged OSH professionals through an in-depth survey, capturing qualitative insights from their daily decision-making process. The responses were analyzed using thematic analysis, an approach that identifies emergent patterns in the responses. Through this process, fundamental attribution error, hindsight bias, confirmation bias, negativity bias, illusory correlation, and outcome bias were identified as recurring biases that influence workplace safety decision-making (Chapman & Chapman, 1969; Fischhoff, 1975; Jones & Harris, 1967; Kahneman et al., 1982; Rozin & Royzman, 2001; Wason, 1960).

The discussion centers on recognizing these biases and how if unaddressed, these can distort risk assessments, foster misplaced accountability, and undermine individuals’ expertise. By recognizing these influences, safety professionals can instead develop frameworks that address operational and systemic factors, and elevate human performance as a strategic asset. The central question guiding this research is: Do conventional safety management systems or safety programs foster a focus on human factors in safety practices?

Literature Review

Safety Frameworks and the Focus on Human Factors

 Human factors and their relationship in the workplace have been studied by many authors as early as the industrial revolution. Emphasis on human error in the OSH industry, mainly in the shape of accountability, emerged later (Dekker, 2019). Specifically, much of this is usually attributed to the work of H.W. Heinrich (Busch, 2019). One of Heinrich’s more notable statements is that workers and their behaviors were one of the principal causes of occupational accidents (Heinrich, 1941). This perspective has had a lasting impact in the OSH industry. To date, many OSH professionals’ perceptions of human roles in safety systems remain influenced by this view, often leading to an unfavorable view on workers’ contribution to safety.

Safety management systems and safety programs are often driven by a combination of voluntary standards and regulatory compliance (Brauer, 2022). Established safety practices usually focus exclusively on adherence to these safety rules and regulations (Hollnagel, 2018). While there is no denying these standards’ positive contribution to the safety industry, they have also unintentionally fostered a perception that the only way to work safely is through absolute compliance (United States Government Accountability Office, 2012). Standards provide a valuable foundation but they must be complemented by other factors such as adaptability, sound judgement, and a proactive safety strategy (Conklin, 2019).

In this context, authors such as Hollnagel (2018) and Dekker (2002) advocate embracing principles of variability and resilience. They highlight that these historically prevalent approaches often overemphasize attribution of human factors, which can limit a more nuanced understanding of complex systems.

Bias

The 1970s marked a turning point for cognitive biases, as their systematic study gained traction through the work of Daniel Kahneman and Amos Tversky (Kahneman et al., 1982.)

Their work laid the foundation for understanding how natural cognitive processes shape human decision-making. Their research revealed how people often make inconsistent or counterintuitive choices due to cognitive biases. While terms vary from formal to informal, the definitions do not vary widely in that they account for how these cognitive phenomena enable us to process information in order to navigate our daily lives (Berthet & de Gardelle, 2023).

It is worth noting that cognitive biases are an inherent and natural feature of human cognition, shaped by the brain’s needs to process vast amounts of information for survival purposes. Rather than being right or wrong, they reflect the mind’s adaptive mechanisms to simplify complex scenarios and allow fast decisions under uncertainty. Acknowledging biases as natural cognitive processes, rather than flaws, fosters self-awareness, objectivity, and allows for a deeper understanding of how we perceive and interpret information (Haselton et al., 2015).

Bias in OSH

Identification of hazards is a core responsibility of OSH professionals. However, the work of Purushothaman et al. (2025) found that in the construction industry, confirmation bias may influence individuals to ignore systematic warnings and assume existing safety measures are adequate. This can result in a failure to see emerging hazards or oppose critical safety updates.

Cognitive biases have also been found to influence enforcement activities coming from workplace safety compliance officers. Research by Heese et al. (2024) demonstrates this, showing that a number of recorded regulatory safety findings and the fines associated with them decreased during sunny weather. This effect was greater when the compliance officers had discretionary judgement in issuing citations. The research evidenced that the absence of predetermined frameworks in non-routine inspections creates conditions where biases may have greater influence.

Finally, accident causation is a key part of the OSH profession and causality has long been discussed since modern notions of industrial safety emerged (Busch, 2019). The work of Maclean and Dror (2021) highlights that anchoring bias influences the determination of causality during investigations, as it causes lead investigators to focus selectively on evidence that confirms their initial hypothesis while disregarding evidence that contradicts it.

The Text Behind the Image

The image below presents a visual challenge. Our perceptions are often shaped by our expectations and focus. Daniel Kahneman’s concept of “what you see is all there is (WYSIATI)” is clearly demonstrated by stereoscopic images (Kahneman, 2011). In this case, the initial perception may be that of an image of a textured pattern. However, the texture conceals a hidden word. By adjusting the viewing perspective, the word can be revealed for a brief moment.

Image 1

The Text Behind the Texture

Note. This is called an autostereogram. Image created utilizing https://www.easystereogrambuilder.com/.

(Tyler & Clarke, Sep 1, 1990), there is a hidden word behind all the texture.

This image requires observers to identify a hidden word, but maintaining awareness without focused attention proves difficult even after one momentarily perceives it. This example illustrates the nature of cognitive biases. Similar to how the hidden text remains present whether visible or not, biases persistently shape our cognitive processes even after we become aware of them (Kahneman, 2011). Knowledge of these biases can help us recognize their influence and how they subtly shape our perceptions, decisions, and judgments.

Importantly, this exercise is not merely a visual puzzle. It is a direct model of how the brain’s information processing can cause it to overlook information. This cognitive tendency is a vital insight for understanding decision-making and hazard recognition in OSH.

Methodology

Approach

This research examines how cognitive biases may influence safety professionals’ decision-making processes within the context of safety programs. Data were collected through a survey instrument designed to elicit safety professionals’ experiences, perspectives, and motivations with regard to key areas related to workplace safety such as accident causation, safety-related performance measurement, incident investigation, and emerging safety concepts (Braun & Clarke, 2021; Van Wassenhove et al., 2022).

Population Criteria

A purposive sampling approach was used to recruit participants (n=60) with diverse experience levels in OSH (Bougie & Sekaran, 2020). The study population included members of a professional safety organization’s online community and one of its separate in-person local chapters. This strategy ensured representation from individuals ranging from those new to the profession to seasoned professionals, working across various industries such as construction, general industry, mining, manufacturing, and maritime.

Survey

Given the study’s aim to capture the nuances of safety professionals’ experiences and its exploratory approach, Braun and Clarke (2021) suggest utilizing a qualitative survey, as this allows for the gathering of rich, in-depth answers related to social processes, as well as prioritizing each participant’s perspectives.

To gather a broad spectrum of responses and insights, the question design incorporated a multifaceted approach that included different types of questions such as open and closed-ended, multiple-selection, and ranking (Bougie & Sekaran, 2020). The question order was randomized to avoid leading participants toward particular themes. The questions employed were drawn from the survey instrument, the complete list of which can be found in Appendix A.

The survey was designed to be highly accessible. It was made available for 14 weeks through multiple channels. Participants for the local chapter were invited through in person requests at the monthly chapter meetings and via email. For the organization’s online community, the survey link was posted multiple times across the online platform.

In order to protect participant identity and guarantee anonymity, no form of personal information was required. Additionally, participation in the survey was completely voluntary, and there was no researcher involvement in the selection of individual participants. Given that the collection of detailed demographic data such as job title, industry, and tenure was purposefully excluded to mitigate priming bias, the potential for subsequent demographic analysis is constrained (Mligo, 2016). As a result, inferences are limited to the defining characteristics of the purpose sample, safety professionals registered in a professional association. For the same reason, quantifying incomplete surveys is not possible, as the anonymous, public link did not track entry or abandonment prior to submission.

Data Extraction

Initial Screening

A data quality screening process was implemented prior to any analysis to guarantee the accuracy and reliability of information. This included a thorough examination of the data, during which incomplete or irrelevant answers were removed from the analysis for the open-ended questions. For the close-ended questions, the results were systematically collated, counted, analyzed, and organized into a spreadsheet (Braun & Clarke, 2021).

Main Data Analysis

For data analysis, the survey questions and responses were categorized and organized in a spreadsheet based on their structure: open-ended, closed-ended, multiple-choice, and ranking scale.

The survey generated rich qualitative data. To analyze responses from 60 participants (n=60), the authors employed thematic analysis. This method offers a framework for analyzing qualitative data, by establishing themes and patterns, as well as connecting the dataset to existing theory and wider context (Braun & Clarke, 2021).

Open-ended Survey Questions

The first step in the data analysis is thoroughly exploring the data to develop a strong understanding of its content and its context. The second step was to create identifiers and key terms to establish the initial set of codes. The third step of analysis involved seeking out recurring themes and patterns within the participant responses. Finally, a systematic approach was utilized to structure the initial set of codes to create a comprehensive framework and ensure analytical consistency. These themes, along with the codes were then cross-referenced with established definitions of cognitive biases and compared against the data set (Braun & Clarke, 2021).

Closed-ended Survey Questions

For the closed-ended, including multiple-choice, and ranking questions, the responses were analyzed to identify trends such as the most frequently selected options, average rating, and ranking distribution.

Data Coding

Survey responses to the question “When performing incident investigations, what are the most common root causes?” were coded using a four‑category scheme designed to capture the primary focus of each participant’s explanation. The Behavior (BE) category included any response that referenced worker actions or behavioral factors. This encompassed observations of worker conduct, compliance with expectations, or the individual’s knowledge and application of policies, procedures, training, or on‑the‑job performance standards. The Organizational (OR) category captured responses that emphasized aspects of the work environment or operational context. Statements referring to physical surroundings, equipment, work areas, workload, task design, or changes to these elements were coded as organizational in nature. The System (SY) category was used for responses that addressed broader organizational systems or performance structures. This included references to policies, programs, procedures, training systems, or any qualifiers or changes related to these organizational mechanisms.

Finally, the Learning (LE) category included responses that highlighted training, learning, or professional development. Mentions of formal instruction, on‑the‑job learning, continuing education, deliberate knowledge updates, or similar learning‑oriented processes were coded within this category.

Once the first set of codes was established a second set of codes was applied to the behavioral (BE) and learning (LE) categories to identify underlying cognitive biases. Responses within the BE category citing rushing, complacency, failure to follow safety rules, and employee shortcuts were interpreted as reflective of hindsight bias, the tendency to see causes as more foreseeable than they were (Fischhoff, 1975).  Within the LE category, responses focused on lack of training, inadequate supervision, and insufficient evaluation of hazards were linked to fundamental attribution error, which in this context is over-attributing causes to individual factors while underestimating situational or systemic influences (Jones & Harris, 1967).

Findings

For the open-ended questions, the answers were carefully examined to identify key themes that were recurrent. These were then organized and labeled according to the specific cognitive bias they reflected which include fundamental attribution error, hindsight bias, confirmation bias, negativity bias, and outcome bias These are discussed in more detail below.

Fundamental Attribution Error

Overemphasizing dispositional factors while underestimating external influences is called the fundamental attribution error (Jones & Harris, 1967). This disposition then leads to the assumption that a person’s actions reflect their inherent traits rather than considering situational factors (Gilbert & Malone, 1995). In the context of OSH, this bias leads to blaming workers when tasks do not go as planned. For example, when asked about causation related to unplanned events, the survey respondents had answers such as “employee shortcuts and supervisors failing to enforce policy,” “employee disregard to safety,” and “complacency, lack of attention, and lack of situational awareness.” These responses reveal a pattern of blaming frontline workers while underemphasizing other factors such as inadequate work design, potential lack of psychological well-being, and conflicting organizational priorities. This singular focus can contribute and limit processes related to safety programs such as risk analysis, training, incident investigation, and workplace interactions (Dekker & Tooma, 2022; Thallapureddy et al., 2023). The effect is illustrated in Figure 1, where a dispositional attribution leads to blaming the worker and ignoring contextual factors.

Figure 1

Fundamental Attribution Error

Note.  In this example, notice the difference between dispositional and contextual.

Hindsight Bias

This phenomenon can lead to an overestimation of predictability. It is more commonly referred to as the knew-it-all-along effect (Fischhoff, 1975). In other words, this bias leads individuals to perceive past events as being more predictable than they were. For example, after a safety event occurs, individuals may believe that the causes of the event were obvious and easily preventable, even though this may not have been the case.

Patterns in the responses for this bias were tied to themes related to workplace training and safety performance measures. Some of the responses include, “I can bring knowledge of past incident/accidents to guide me in preventing new ones” and “Life experience has demonstrated that human behavior is predictable.” Collectively, these responses imply that the variability of day-to-day tasks should be obvious and predictable. This approach does not take into account the nuanced and dynamic nature of everyday work. Specifically, deviation from procedure is not necessarily related to an increase in unplanned events (Nazaruk, 2023). This also exemplifies counterfactual thinking, a process where we mentally construct alternative versions of how events unfolded instead of accepting established facts. This process often contributes to overlooking the perspectives of those directly involved (Epstude & Roese, 2008). The role of hindsight bias in oversimplifying accident causation is demonstrated in Figure 2.

Figure 2

An Example of Hindsight Bias

Note.  An example of how hindsight bias can cause oversimplification of event causality (Cook et al., 1998).

Confirmation Bias

This cognitive phenomenon influences how we gather, interpret, and remember information. In essence, it is the tendency to only seek out what reinforces existing beliefs (Wason, 1960). A common pattern that emerged in the responses was the particular selection of safety theories, attribution of root causes, and evaluation methods.

Many of the survey respondents frequently assumed that safety events occur primarily due to a lack of worker knowledge or awareness, leading them to suggest retraining as the primary solution. For example, one survey respondent stated, “Following accidents where the procedure was not followed, when workers demonstrate inadequate understanding of the risks imposed by their behavior.” These thought processes risk circular reasoning. It presumes worker negligence based on non-compliance, which is then used as evidence of negligence itself, while overlooking broader organizational and environmental contributors (Kahneman, 2011; MacLean & Dror, 2021). Figure 3 illustrates the narrow focus of confirmation bias: the selective search for information that confirms preconceived notions.

Figure 3

Confirmation Bias

Negativity Bias

The concept of negativity bias, is a psychological phenomenon where individuals tend to give greater weight to negative experiences, information, and decision-making compared to positive or neutral ones (Kahneman et al., 1982; Rozin & Royzman, 2001). This means that people tend to notice, remember, and react more strongly to negative stimuli, even when it is not objectively more significant.

Many of the respondents attributed accident causation to worker behaviors they perceived as inappropriate or unsafe within the context of workplace safety. However, these behaviors may reflect normal human variability within a work environment and do not inherently result in negative outcomes (Hollnagel et al., 2006). For example, some of the responses included “employee shortcuts,” “rushing,” and “mind not on task.”

Framing accidents as a result of poor decisions reduces their complex nature, disproportionately blames individuals, and neglects organizational or systemic factors. This pattern was also evident in responses related to workplace safety training, where the training strategies suggested by the respondents emphasize addressing negative outcomes rather than fostering a proactive safety approach.

By contrast, when prompted to identify learning opportunities in daily work, most respondents struggled to name examples, offering replies such as “none,” “N/A,” “Not familiar with any,” and “none come to mind” (Conklin, 2019; Dekker & Pitzer, 2016; Nazaruk, 2023). Figure 4 exemplifies the negativity bias, showing a skew toward negative framing in decision-making.

Figure 4

Negativity Bias

Note.  An example of how negative decisions hold more “weight” than routine tasks.

Illusory Correlation Bias

This bias is defined as the perception of relationship between variables that does not exist. Such bias often arises because the relationship aligns with our expectations, pre-existing beliefs, or because of the distinctiveness of certain events (Chapman & Chapman, 1969).

When asked about event causality, the respondents demonstrated a strong focus on individual accountability, often attributing actions or outcomes to human error (Hamilton & Gifford, 1976). The data analysis revealed a pattern of citing specific human factors, including “rushing, fatigue, complacency, or frustration” as well as broader concepts like “human behavior” and general behavioral tendencies. However, this emphasis risks oversimplifying complex scenarios by framing mistakes as isolated choices, potentially overlooking systemic or contextual influences (MacLean & Dror, 2016).

The respondents’ safety training approach often assumed a one-size-fits-all solution. For example, responses such as, “Generally any time there is a near miss, first aid case or injury/illness, when employees are not following policy and procedure,” have an underlying assumption that with increased training intensity there will be a decrease in unplanned safety events. This perspective presupposes a straightforward and linear causal link between training intensity and a specific safety outcome (Reiman & Rollenhagen, 2010).

Table 1 illustrates questioning the preconceived notions about the relationship between following safety procedures and safety events. For example, if the belief is that not following procedures always leads to an unplanned outcome, cell “D” might challenge this assumption (Frost, 2019; Reiman & Rollenhagen, 2010).

Table 1

The Contingency Table Test

Outcome Bias

Outcome bias occurs when an individual judges a decision or an action by its result and not the process that led to taking the decision in the first place (Reiman & Rollenhagen, 2010). This includes evaluating the decisions of others by this same lens. A common pattern in the responses was focusing on what happened, rather than why it happened. For example, when asked about organizational learning as it relates to safety, there was an overreliance on learning from lagging indicators such as incident rates, injury statistics, or workers compensation claims.

This pattern was also evident in how incidents were attributed to individual behaviors. Specifically, the respondents frequently focused on the consequence of an event rather than the context in which decisions are made. For example, the responses “The number of injuries and visual inspections” and “Employee’s working to fast or failing to recognize and reduce the hazards associated with the work task” anchored the outcome of events to observable individual actions. These attributions overlooked the contextual conditions by focusing disproportionately on personal accountability and consequences (MacLean & Dror, 2016).

Figure 5

Outcome Bias

Discussion

The recurrence of fundamental attribution error, hindsight bias, confirmation bias, negativity bias, illusory correlation bias, and outcome bias across the safety professionals’ survey responses provide an answer to the research question: Do conventional management systems or safety programs foster a focus on human factors in safety practices? The findings indicate that these systems, unintentionally foster a focus on individual human factors in the form of blame.

Specifically, this pattern was evidenced by the tendency to over-attribute incident causality to worker behavior (fundamental attribution error) and to oversimplify complex events into predictable failures (hindsight and outcome bias). These tendencies align with the traditional compliance-based frameworks as established by Dekker (2019). By continuously focusing on personal accountability these biases direct attention away from systemic weaknesses.  This pattern is further cemented by confirmation bias, which in the context of OSH, causes safety professionals to seek information that validates pre-existing beliefs such as inadequate training, and negativity bias, which focuses on the negative outcome of an event over the complex, normal variability of work (Hollnagel, 2018).

The practical implication for safety professionals is a fundamental shift in focus. Moving from a compliance-based, blame-oriented mindset, to designing systems that anticipate human variability. This means moving beyond judging a decision based on its result (outcome bias) or assuming a causal link where none exist (illusory correlation), toward embracing work that anticipates and accommodate how normal work actually happens. This reframes human performance not as problem to focus on, but as a central component in the complex interaction between tools, tasks, and goals.

Limitations

The nature of this study is based on participants’ perspectives and experiences. This data is qualitative and subjective by default. Furthermore, this study reflects the participants’ knowledge and perspectives at the time the survey was conducted. As human nature is dynamic, it is important to acknowledge that these perspectives and levels of knowledge are not static. They may change over time due to factors such as lived experiences and exposure to new information.

It is important to acknowledge that the self-reported nature of the data raises the possibility of social desirability bias. This occurs when participants consciously or unconsciously provide responses influenced by perceived social expectations rather than their own behaviors or beliefs. The authors took precautions to relieve this pressure and encourage open participation. Such measures include delivering the survey through electronic methods allowing participants to complete it privately and at their own pace. Finally, the participants were informed about the anonymity and confidentiality measures before they began the survey.

The survey was distributed via a public link that was deliberately disseminated through targeted OSH channels and communities, a purposive strategy that, by its nature, makes the total number of views unknown and a response rate incalculable.

Although purposive sampling limits broad generalizations, the study’s strength lies in its exploration of real-world scenarios and challenges that are widely recognized within the OSH profession. The findings reveal patterns and insights applicable to the shared professional context in which OSH professionals operate. Therefore, the study’s findings offer valuable insights that can be transferred and applied to similar OSH frameworks and practices.

Conclusions

The study explores the significant role of cognitive biases in shaping OSH professionals’ perceptions, judgements, and responses to workplace practices. The study’s findings underscore the complexity of the human element in safety management systems and programs. By examining OSH professionals’ experiences and perspectives through a qualitative survey, the research identified prominent themes related to cognitive biases such as fundamental attribution error, hindsight bias, confirmation bias, negativity bias, illusory correlation bias, and outcome bias.

Understanding cognitive biases is not about eliminating human error, but about creating a more resilient and adaptive system that recognizes human complexity. By embracing this approach safety professionals can shift workplace safety from focusing solely on compliance, to a dynamic process that prioritizes human expertise and fosters adaptability.

By actively identifying where and how these cognitive processes occur, OSH professionals can foster a truly collaborative safety approach that enables human potential.

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Appendix A

Survey Questions

McKell Sanderson

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McKell Sanderson, Ph.D., CSP, CHST, CHMM, ARM, is a regional safety manager for Amazon Web Services and doctoral chair at Capitol Technology University. He has more than a decade of experience in the EHS and compliance industry, and his practical experience spans many industries including insurance, technology, construction, and higher education. His specialty is occupational risk management. Sanderson is a professional member of ASSP’s Lower Columbia Basin Chapter.

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Antonio Carlos Bacci di Capaci Fiallos

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Antonio is an accomplished and dedicated professional in the field of Occupational Health, Safety, and Environment (OHSE). He has worked in biotechnology, innovative technology industries, manufacturing, and mining. He is the current holder of the following designations: CSP, CHMM, and CSHM. As an avid learner himself, he passionately encourages everyone and anyone to pursue ongoing education in any way, shape or form. He is a professional member of ASSP’s Columbia-Willamette Chapter.

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Areas: OSH / Environmental Management
Categories: General