Workers may fall prey to certain cognitive biases as shortcuts that result in judgment errors and risky decisions, such as risk compensation. The risk-compensation bias argues that individuals adjust their at-risk behaviors to achieve a balance between potential risks and benefits and thereby maintain a target level of risk. Derived from external (e.g., task or environmental-related) and internal (e.g., individual characteristics) sources, risk compensation ultimately influences an individual’s (deliberative, affective, and experiential) risk perception as a central predictor of health and safety-related behaviors and certain risky decisions. Decision making under risk is mainly studied at the individual level in the construction-safety setting. However, drawing on social influence and behavioral intention theories, coworkers’ risk-taking serves as an “extra motive†of risk-taking behavior among workers in the workplace. Thus, studying the risk-compensation effect in the construction environment can become more complicated given that construction workers work in groups, and coworker behavior can influence safety-related behavior. Furthermore, the effects of heat exposure and subsequent heat stress might translate into an increased risk of injury caused by physical discomfort, fatigue, and reduced vigilance that can influence worker emotional state and risk perception, and lead to cognitive failure, misperceiving hazards, and neglecting precautionary behavior. Accordingly, this multidisciplinary project addresses these gaps by integrating psychological science, artificial intelligence (AI), and advances in construction safety to deliver a novel theoretical platform and empirical process to understand the latent changes in worker decision dynamics following an intervention for greater protection from injury.
The specific objectives of this study are to (1) examine the extent to which individuals’ characteristics and psychological states, along with task and environmental factors (e.g., time pressure, extreme heat) influence workers’ at-risk decisions; (2) determine the role of risk compensation bias on team risk perception, decision making, and work behavior; and (3) develop a multidimensional AI model to identify at-risk workers and interpret their risky decision-making, using limited attributes including individual, task, and environmental-related factors. To achieve these objectives, a multi-sensor immersive 360 mixed-reality environment that consists of passive haptics and environmental modalities is used to raise the workers’ sense of presence, capture their realistic responses to safety features during various current and future construction tasks. A combination of qualitative and quantitative measures serve to investigate the underlying mechanisms of workers’ risk-compensatory behaviors and decisions. The measures derive from location-tracking sensors, vision-based sensors, wireless neuropsychological and cognitive brain monitoring (fNIRS), eye-tracker, photoplethysmography (PPG) and galvanic skin response (GSR) psychophysiological sensors, semi-structured interviews, demographic, and psychographic surveys. The collected data constitutes information about workers’ behavioral changes simulated using agent-based modeling, and used to develop a multidimensional predictive model to minimize the likelihood of risk compensation and to prevent incidents and injuries. The project outcomes have the potential to impact the performance of a nationwide industry and create a novel platform for enhancing the national research and education infrastructure. They advance protection mechanisms for thousands of American workers and save estimated billions of dollars in financial costs per year in the United States.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
