Industrial accidents are a critical problem affecting u.s. industry today. They account for 3.2 million disabling injuries and 9,000 deaths annually, costing upwards of $110 billion a year according to the National Safety Council's latest data. Reducing the incidence of industrial accidents is one of the primary concerns of safety engineers. To reduce accidents, workers are trained in safe procedures and provided with personal protective equipment (PPE) where necessary. However, training and PPE are only effective when workers comply with the trained procedures and use the PPE. Companies use various reward systems and negative reinforcement to encourage workers to comply. Several factors may affect compliance behavior. When workers perceive that management has a strong commitment to safety, they may be more influenced by safety policies. On the other hand, if the company's incentive system for productivity overshadows the safety program, they may sacrifice safety compliance to achieve greater production. Risk perception is also likely to influence compliance. If a worker does not perceive a hazard as being either likely or serious, they are less likely to comply with safe behavior at the expense of productivity. Some factors may affect risk perception. Workers who have personally experienced the consequences of an industrial accident should be more likely to perceive a task as risky and comply with safe behaviors. On the other hand, long work service without an accident is likely to have the opposite affect. This paper outlines the empirical development and validation of a model to predict safety compliance. Compliance with company PPE regulations were observed at a manufacturing facilityfor several months. Jobs included various types of manufacturing processes, assembly tasks, shipping, and receiving. Data for each observation included details on the age and experience of the worker, the worker's perceptions of job difficulty and management's commitment to safety. Workers filled out surveys from the Jackson Personality Inventory Revised (JPI-R) to measure personal characteristics of the workers such as risk-taking and anxiety.
The purpose of this research was to develop a methodology that would evaluate employees' personality traits, demographic characteristics, and workplace parameters to predict safety compliance along with the moderating effect of risk perception. One hundred and twenty five employees of a manufacturing facility were given questionnaires to gather their demographic and perception information. Surveys were also used to measure their personality characteristics, and periodic observations were recorded to document employee's safety compliance. A significant correlation was found between compliance and the worker's perception o f management's commitment to safety (r = 0.27, g < 0.01), as well as with gender (r =-0.19, p < 0.05). Females showed a significantly higher average compliance (78%), than males (69%). These findings demonstrated the value of developing a model to predict safety behavior that would assist companies in maintaining a safe work environment, preventing accidents, ensuring compliance, and reducing associated costs.
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