Modern complex safety-critical socio-technical systems (STSs) operate in an environment that requires high levels of human-machine interaction. Given the potential for catastrophic events , understanding human errors is a critical research area spanning disciplines such as management science, cognitive engineering, resilience engineering, and systems theory. However, a research gap remains when researching how errors impact system performance from a systemic perspective.
This dissertation employs a systematic methodology and develops models that explore the relationship between errors and system performance, considering both macro-organizational and micro-worker perspectives. In Essay 1, the focus is on how firms respond to serious errors (catastrophic events), by exploring the oscillation behavior associated with the organizational learning and forgetting theory. The proposed simulation model contributes to the organizational science literature with a comprehensive approach that assesses the firm's response time to "serious" errors when the firm has a focus on safety with established safety thresholds. All of these considerations have subsequent impact on future performance.
Essay 2 explores the relationship between safety-critical system's workers' workload, human error, and automation reliance for the Belgian railway traffic control center. Key findings include a positive relationship between traffic controller performance and workload, and an inverted U-shaped relationship with automation usage. This research offers new insights into the effects of cognitive workload and automation reliance in safety-critical STSs. Essay 3 introduces a calibrated System Dynamics model, informed by empirical data and existing theories on workload suboptimality. This essay contributes to the managerial understanding of workload management, particularly the feedback mechanism between operators' workload and human errors, which is driven by overload and underload thresholds. The model serves as a practical tool for managerial practitioners to estimate the likelihood of human errors based on workload distributions.
Overall, this dissertation presents an interdisciplinary and pragmatic approach, blending theoretical and empirical methodologies. Its broad impacts extend across management science, cognitive engineering, and resilience engineering, contributing significantly to the understanding and management of safety-critical socio-technical systems. / Doctor of Philosophy / This dissertation is motivated by the increasing autonomy in infrastructure systems designed to enhance safety performance. Yet paradoxically, we continue to witness system failures leading to catastrophic disasters. High-profile incidents such as the Metro-North train derailment in New York City, the Boeing 737 MAX plane crashes, and the Challenger and Columbia space shuttle accidents highlight this contradiction. This research delves into safety-critical systems where the intricate collaboration between humans and machines is crucial, and where even minor human errors can lead to disastrous consequences.
This dissertation is presented in three parts. In the first part I examine how firms react to serious errors. The study focuses on their learning processes following safety incidents and the potential for these lessons to be forgotten over time. I introduced a simulation model grounded in the organizational science literature, offering deeper insights into how companies respond to errors, including changes in safety focus, safety culture, and policy, and the impact of these factors on future company's performance.
The second part shifts to a worker-centered perspective, exploring the relationship between workload, performance, and automation usage among traffic controllers. The findings indicate that while performance can improve with an increase in workload up to a certain threshold, excessive reliance on automation may lead to a decline in performance. This part of the study sheds light on how cognitive workload and technology usage influence operators in safety-critical roles. The final part of the dissertation presents another simulation model, this time focusing on how workload, and the resulting stress and boredom due to workload, influence the likelihood of errors. Utilizing real operational data from the Belgian railway transportation system, this model aids managers in understanding how to optimally balance workloads to minimize error risks.
Overall, this dissertation takes an interdisciplinary and pragmatic approach, merging theoretical concepts with empirical data. Its extensive impact spans management science, cognitive engineering, and resilience engineering, significantly enhancing our comprehension and management of safety-critical socio-technical systems.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/118620 |
| Date | 09 April 2024 |
| Creators | Liu, Ning-Yuan |
| Contributors | Industrial and Systems Engineering, Triantis, Konstantinos P., Roets, Bart, Ghaffarzadegan, Navid, Srinivasan, Divya |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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