## 5 Limitations and Future Direction  
The following are the potential limitations of the current study: First, the levels of automation in this study require further diversification. We designed the automation tasks with only two levels, based on the readability of the sentences. However, two levels may not be enough and too simple to describe a wide range of HAI variation. Therefore, the automation levels should specifically be defined not from the system design perspective, but from the user operation perspective. One viable user-oriented approach is [PERSON]'s four stages of automation [4]. Since information processing can be acknowledged as continuous and sequential, rather than discrete processing, designing the distinctive levels of automation will be challenging. However, verifying the automation effects on task performance in more detailed and explicit levels or degrees of automation should be considered.  
Second, the results of the study are limited to simple office automation and the task performance was evaluated only in terms of time and accuracy. Despite the advantage and value in the application of office automation, it does not have enough variability and flexibility in terms of the changes in the level or degree to evaluate the effects of the automation. More specified automation levels or degrees would provide detailed performance measures, so that the effects and adoption patterns of new automated functions can be identified. In addition, the task completion time and accuracy cannot be a complete set of task performance measures. Additionally, the performance indicatorsincluding relatively objective criterion measures, such as job knowledge tests and production rates, the dimensionality of job performance, and validity estimates against task performance (e.g., ability, personality, etc.) need to be considered [45].  
Third, this study compared task performance with subjective workload measures in automation task performance. However, the two groups of measures were evaluated by different data collection approaches. While task performance measures gather data during the experiments, subjective workload measures collect attitudes or perceptions using a post-hoc survey questionnaire. However, the different data collection approaches may hinder a direct comparison between the two types of measures. Therefore, to minimize the possible biases from the different types of data, this study used standardized values for task performance and the Likert scale for subjective workload measures. These corrective approaches make it possible to compare the measures indirectly.  
To overcome the aforementioned limitations, several further studies are suggested: first, the automation tasks should incorporate wider types of automation with specific definitions of the type, degree, and level of automation. Second, the experiment requires sufficient time to cover the patterns of adoption and learning. Such an experimental design will enable researchers to investigate task performance changes between adoption, boredom, and fatigue as the automation is extended. It will also allow researchers to investigate the changes in trust or frustration over time. Last, another task performance measuring approach can be considered, for instance, physiological measures by various sensors may supplement the objective and subjective evaluation of task performance. Since the concept of task performance seems to be difficult to define, physiological measures, such as heart rate or skin conductance, can provide additional information to aid the evaluation of the effects of automation on task performance.