Chapter 44: Workplace Safety and Personality

Researchers have historically investigated the concept of an ‘accident prone’ personality by categorizing predictive traits based on their underlying mechanisms,. These mechanisms include attention control, where traits like impulsiveness and distractibility lead to increased accident rates due to a lack of control over risky behaviors and task attention deficits. Traits related to arousal levels, such as sensation-seeking (a desire for intense stimuli), boredom proneness, and risk-taking orientation (a narrower focus on physical danger), consistently correlate positively with unsafe actions like aggressive or risky driving and work injuries,. Additionally, traits related to internal orientation, such as an external locus of control, are linked to a higher likelihood of accidents because individuals feel they have weak influence over life events, while Extraversion has also been associated with increased accident rates compared to Introversion,. When applying the Five-Factor Model (FFM) of personality, studies frequently identify low Conscientiousness and low Agreeableness as the most consistent negative predictors of safety criteria, meaning individuals scoring low on these factors are more prone to involvement in workplace accidents and violations,. Current research suggests moving beyond broad factors to use more specific narrow traits, or facets, and context-specific measures, which are often found to have greater predictive efficacy for specific safety behaviors,. The relationship between personality and safety is placed into context using a process model, which explains that personality characteristics influence safety outcomes indirectly by affecting crucial steps like risk recognition and the decision to avoid a hazard. This complex interaction accounts for the moderate, though consistent, predictive power of personality in safety outcomes, underscoring the necessity of considering safety criteria carefully, often requiring multiple data sources to combine objective metrics (e.g., accident reports) and subjective self-reports,.