## 4 Discussion
### Effects of Automation on Overall and Subscale Workload Measurement  
While we observed a clear distinction of subjective workload between Routine and Failed automation, mixed patterns were shown by automaton levels (see Table 3). Participants perceived heavier workload in High and Failed automation than in Low and Failed, but there was no significant difference in Routine automation by different automation levels. These patterns could be interpreted as meaning that participants perceived heavy demands or more efforts are required when High level automation is failed, whereas they felt similar amounts of demands or efforts regardless of automation levels. This mixed pattern was also shown in the subscales: while all subscales in Routine automation indicated lower scores than those in Failed automation, no distinctive advantages in Low automation were reported subscale ratings. The automation levels significantly affected frustration and mental demand in both automation status. Perceived performance and temporal demand in only Failed automation. These results suggested that the effects of automation levels in the office automation of this study were less notable than those of automation statuses. More specifically, automation levels influenced the subscales in Failed automation. Except for effort, other subscales in High and Failed automation were higher than those in Low and Failed automation. Whereas the task performances (task completion time and task accuracy) were similar outcomes, the subscales of subjective workload were distinctively different in Low and Failed and High and Failed automations. This discrepant result may be caused by different traits of two measures. Task performance can be measured by an empirical approach, and subjective workload refers to the portion of the individual's limited capacity and invested effort to perform a given task [31]. However, these two measures are not always well-matched. Humans tend to perceive and respond differently to the overall experience of whole-task scenarios and instant reactions [32]. Although the measuring method of the subjective workload is convenient, there are always issues as to whether any form of self-report accurately reflects respondents' \"true\" perceptual experiences [33; 34]. To establish the validity of ratings of perceived performance, several studies suggested bringing such subjective ratings under experimental control by demonstrating their association with objective factors [35; 36; 33]. The gap between empirical measures and subjective self-report measures of the effects of automation is considered a potential cause of inconsistent outcomes in different automation. The effort and perceived performance were higher than other subscales, and their scores exceeded the midpoint threshold on the NASA-TLX subscale. The midpoint threshold has been applied to access an unsustainable demand in other domains [37; 38]. However, these specific sustainability thresholds have not been established within HAI. Based on the results in this study, future research should focus on minimizing effort and perceived performance. These findings suggest that providing automation for decision support may aid in smooth information processing and efficient planning procedures. If the automation delivers supportive information with positive HAI, it may reduce subjective workload for operators in unexpected automation failures. A pattern emerged from the data between trust levels and subjective workload. Reported workload differed significantly for the participants when there was a deviation from the expected trust level. Specifically, when the trust level was lower than expected, all NASA-TLX subscales but frustration was significantly higher than cases that were rated at or higher than the expected trust level. While goal expectation has been studied in education and training and acknowledged as contributing to workload demand and workload variability, the impact of trust on task demand has not been quantified [39].