For the mental and physical wellbeing of nuclear power plant (NPP) reactor operators (ROs) it is pertinent these work environments take advantage of automation, to an appropriate extent, to reduce workload and increase performance. With automation, RO resources can be better distributed to make sure NPP operations are running smoothly and efficiently. However, inappropriate automation may put ROs at risk of becoming complacent and slow to react, thus unable to perform their job in emergency situations. In this study students acted as NPP ROs and interacted with different tasks and levels of automation. Since NPPs are becoming more digitalized it is important to understand how these changes are going to affect operators' performance and perceived mental workload (MW). Individual differences are also considered, as not everyone is going to have the same reaction to these changes. Results of this study indicate that an increase in automation decreases time to react to the automation requesting input. However, there were significant differences between perceived MW such that higher MW was reported in the higher level of automation for checking and responding tasks. Personality traits can play a large role in how ROs respond to and work with automation. In this study, personality (i.e., Big 5) was not correlated to any MW measures but was positively correlated with perception of automation competence and usefulness in the lower automation condition. When compared with previous iterations of this study that had no automation, both low and high LOA significantly reduced perceived workload. This study's findings enhance awareness of individual differences and their implications on ROs' perceived MW and automation adoption and the importance of upcoming changes in NPPs to ensure optimized RO vigilance and performance.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2654 |
| Date | 01 January 2022 |
| Creators | Schreck, Jacquelyn |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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