In the early 1980s, the integration of behavioral sciences into organizational safety sparked considerable interest among safety professionals. This period marked the exploration of behavioral observation as a key technology in enhancing workplace safety. Behavioral observation, rooted in the principle that behaviors are observable and measurable acts, presented an opportunity to establish a statistical link between specific behaviors and the likelihood of accidents. This approach aimed to provide a more precise measure of workplace safety compared to traditional methods.
Traditionally, safety metrics were primarily based on lagging indicators, such as data from accident investigations. This approach led to reactive management, where organizations addressed risks only after a pattern of injuries emerged, often resulting in a sarcastic attitude among workers towards risk identification and prevention. The limitation of these lagging indicators was further compounded by the reliance on reported accident data, which often suffered from underreporting and inaccuracies.
The concept of behavioral observation introduced proactive metrics to supplement the existing lagging indicators. However, the subjectivity inherent in this method posed challenges, as observers were required to make value judgments about the safety of observed behaviors. This led to the development of two new techniques: pinpointing and operational definition. Pinpointing involved breaking down complex behaviors into simpler, observable actions, while operational definition involved defining multiple criteria to evaluate behavior as safe. Both methods required careful selection of behaviors to observe, raising questions about their relevance and importance.
To address these challenges, Pareto Analysis was applied to identify behaviors most significantly associated with accident reduction. This approach involved analyzing accident and near-miss data to prioritize behaviors for observation. As the method evolved, the list of behaviors grew more comprehensive and industry-specific, leading to the creation of more refined observation checklists.
Initially, observation checklists were lengthy and required extensive training, but they evolved to focus on a few significant behaviors for quicker internalization and easier observation. This approach, however, revealed that behaviors not on the checklist could resurface as problems, prompting the development of dynamic checklists that could be adjusted over time.
As technology advanced, software applications were developed to analyze observation data, with "Percent Safe" becoming a common metric. This metric, however, had limitations as it reflected behavior under observation, which might differ from typical behavior.
Behavior-Based Safety (BBS) strategies also included ongoing Pareto Analysis of accident data to maintain focus on critical behaviors. Effective communication and collaboration between Steering Teams and other organizational levels were crucial for the success of these initiatives. These teams not only developed action plans to improve safety but also to enhance the observation process itself.
In summary, the evolution of BBS from a subjective approach to a more sophisticated, statistically driven process underscores the importance of measuring human behavior in safety improvement initiatives. This evolution highlights the potential benefits of applying these measurement techniques to various processes involving human elements.