Systematic Literature Review Of Workload Calculation Using NASA-TLX
DOI: 10.52155/ijpsat.v34.2.4619
archive: archived pipeline: cataloged verified
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Summary
This systematic literature review and meta-analysis investigates the workload of Air Traffic Control Officers (ATCOs) in Indonesia prior to the COVID-19 pandemic. The study addresses the critical need to establish baseline workload data to inform decision-making and future research, particularly given the significant disruption to air traffic caused by the pandemic. The primary research questions focus on determining the calculated ATCO workload using the NASA Task Load Index (NASA-TLX) and identifying the most dominant dimensions within that workload across various Indonesian airports. The methodology employed a quantitative meta-analysis, synthesizing data from seven primary studies conducted between 2018 and 2019. These studies, sourced from the Indonesian Aviation Polytechnic Library, utilized the NASA-TLX instrument to assess workload at five major airports: Jakarta, Makassar, Denpasar (Bali), Pontianak, and Pekanbaru. The NASA-TLX measures workload across six dimensions: Mental Demand, Physical Demand, Temporal Demand, Performance, Effort, and Frustration. The researchers aggregated the Weighted Workload (WWL) scores from these primary studies to identify trends and dominant factors. The analysis included statistical tests for normality and correlation to validate the relationships between workload variables and outcomes such as stress, situation awareness, and safety performance. The findings reveal that Mental Demand and Effort were consistently the highest-rated dimensions across the different airports, indicating that cognitive processing and the exertion required to maintain performance are the primary drivers of ATCO workload in Indonesia. Specific results varied by location: in Jakarta, Mental Demand was the highest factor, followed by Performance and Effort. In Bali, Mental Demand was also highest, with a strong negative correlation (-0.698) between workload and situation awareness, suggesting that higher workload significantly impairs situational awareness. In Makassar, the study highlighted strong correlations between workload and stress, with "Working Hours" and "Responsibility" being key indicators. Pontianak showed Effort as the dominant factor, while Pekanbaru identified Mental Demand as the highest, with workload contributing 88.7% to safety performance variance. Overall, the meta-analysis confirms that cognitive load, rather than physical exertion, is the most significant component of ATCO workload in the Indonesian context. The significance of this study lies in providing a consolidated, pre-pandemic baseline for ATCO workload in Indonesia. By identifying Mental Demand and Effort as the critical stressors, the findings offer actionable insights for aviation authorities and airport operators. The results suggest that interventions should focus on reducing cognitive load through sector restructuring, improved automation, and targeted training to enhance situation awareness and safety. This data serves as a crucial reference for policymakers to compare against post-pandemic workload conditions and to develop strategies that ensure optimal workload levels, thereby maintaining aviation safety and controller well-being.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | pdftotext | — | — | 4 | 2026-08-10 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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