## 1 Introduction
The possible LSA renders all sorts of operators' errors and a mistrust in automation or a lack of proper understanding [13; 14], that is, errors where operators failed to respond to a critical situation if the automation failed to alert them properly or where operators followed incorrect advice of automation without detecting this failure. Interestingly, modified meta-analysis confirmed that the task performance is maintained at a similar level with increasing DOA and follows a \"flat\" function up to a certain critical point, which mean that a human operator in Failed automation is not as vulnerable from automation failure if the automation functions at the lower level (e.g., offering options) and earlier information processing stages (e.g., information acquisition or information analysis) [3]. However, this assertion is based on the results from only 18 studies, and statistical power is not enough. Moreover, heterogeneous types of automation in the studies seem not to provide consistent performance results, and the meta-variables form performance measures and subjective measures in workload and situation awareness may not yield clear statistical conclusions. Office automation is one of the easily accessible automated systems and widely used in various areas. Transitions to modern office automation require learning, processing of new data inputs, and substantial adaptation by the office workers. Such transformational adoption of human-system interactions in office automation are often driven by meaningful user-oriented benefits. The improved work performance by office automation is usually understood by the perspective of automation technology; however, there is little consideration of the mental and physical costs to users. Closure of this gap is necessary to better understand the implications of office automation on human-system interactions and to the day-to-day office work performance. As a result, understanding how office automation affects user (worker) experience and workload can be considered a major part of human-system integration efforts in everyday working environments [15; 16]. Workload is a broad, multifaceted term that encompasses the human \"cost\" of performing a task [17]. Despite the concern with high workload in a modern office environment and its links to performance degradation, to date few efforts have been made to design office technologies with office workers and team workload in mind [18]. This study attempted to evaluate the relationship between user work performance and subjective workload by different perceived trust levels in office automation. There have been few empirical efforts attempting to ratify this HAI concept in general automation use. Furthermore, the trade-off can be contrastingly perceived in subjective measures (i.e., subjective workload, trust). Although a number of studies have examined the user workloads and performance in different DOAs, the results were mixed [19; 20; 21], and relatively few addressed the issues of joint human-automation performance in imperfect automation conditions. Therefore, in this study, we designed automated proofreading tasks as an exemplary model of office automations that users can easily adapt to, and evaluated the task performance measures (i.e., task completion time and accuracy) at different levels and statuses of automation. To understand how automation in human-system interactions impact demands and efforts, we also measured subjective workload by the National Aeronautics and Space Administration (NASA) Task Load Index (TLX), which includes multiple subscales to subjectively evaluate user demand [22]. Subjective workload along with perceived trust levels described the current status of operative demand and identified the potential impact of HAI. The remainder of this article is organized as follows: in Section 2, the automation design, experimental setup, and data analysis procedures which will be used in the experiment are described. The experimental results are presented and analyzed in Section 3.