Evaluation of Mental Workload Using the NASA-TLX Method on Call Center Operators at PT. XYZ Makassar
DOI:
https://doi.org/10.56882/jisem.v3i2.49Keywords:
Call center, Mental workload, NASA-TLX, Workload managementAbstract
This study examines the mental workload experienced by call center operators at PT XYZ Makassar using the NASA-TLX method. High mental workload can impact service quality, productivity, and employees' psychological well-being, particularly in high-pressure service industries. The aim of this study is to identify the dominant dimensions of mental workload and analyze their relationship with the work conditions of call center operators. A mixed-methods approach was employed, focusing on descriptive quantitative analysis, complemented by qualitative data through semi-structured interviews. The primary instrument used was the NASA-TLX, which measures mental workload based on six dimensions: mental demand, physical demand, temporal demand, performance, effort, and frustration. The study involved 20 call center operators at PT XYZ Makassar. The findings reveal that 60% of operators experience high levels of mental workload, with effort and mental demand being the primary contributing factors. These findings indicate the need for improvements in workload management, such as restructuring work schedules, stress management training, and enhancing psychosocial support to alleviate excessive mental workload. This study contributes to the development of cognitive ergonomics theory in the service sector context and provides practical recommendations for improving employee well-being and operational efficiency. Further research could explore the long-term effects of mental workload on employee health and performance in other sectors.
References
C. Carissoli, L. Negri, M. Bassi, et al., “Mental workload and human–robot interaction in collaborative tasks: A scoping review,” International Journal of Human–Computer Interaction, vol. 40, no. 1, pp. 123–140, 2024. [Online]. Available: https://www.tandfonline.com/doi/abs/10.1080/10447318.2023.2254639
A. Blank, E. Kosar, E. Karlidag, Q. Guo, S. Kohn, "Human-robot cooperation during complex or harmful tasks operated from a distance," Procedia CIRP, vol. 98, pp. 46-51, 2021. [Online]. Tersedia: https://www.sciencedirect.com/science/article/pii/S2351978921002043.
Wu, Y., Zhang, Y., & Zheng, B. (2024). Workload Assessment of Operators: Correlation Between NASA-TLX and Pupillary Responses. Applied Sciences, 14(24), 11975. [Online]. Tersedia: https://www.mdpi.com/2076-3417/14/24/11975
N. Braarud, P. Ø. (2020). "An efficient screening technique for acceptable mental workload based on the NASA Task Load Index—development and application to control room validation," International Journal of Industrial Ergonomics, vol. 75, p. 102914. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0169814119302847
F. Oktaviana, C. Basumerda, and M. F. N. Maghfiroh, "Operator mental workload analysis using NASA-TLX and RSME methods (case study in warping division PT. XYZ)," AIP Conference Proceedings, vol. 2891, no. 1, p. 070014, 2024. [Online]. Available: https://pubs.aip.org/aip/acp/article/2891/1/070014/3295035/Operator-mental-workload-analysis-using-NASA-TLX
H. A. Priska, K. Aurellia, F. A. Putri, A. Zaidan, and C. Basumerda, "Mental workload analysis of employees in the customer care department of PT. XYZ using NASA-TLX method," Proceeding International Conference on Religion, Science and Education, vol. 1, pp. 735–738, 2022. [Online]. Available: https://sunankalijaga.org/prosiding/index.php/icrse/article/view/861
N. Koundal, A. Abdalhadi, and M. S. Al-Quraishi, “Effect of interruptions and cognitive demand on mental workload: A critical review,” IEEE Access, vol. 12, 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10485405/
D. Tao, K. Yang, T. Zhang, X. Qu, "Mental workload measurement using EEG and its implications for human-robot collaboration," Applied Ergonomics, vol. 92, p. 103356, 2022. [Online]. Tersedia: https://www.sciencedirect.com/science/article/pii/S0003687022000837.
A. Brunzini, F. Grandi, and M. Peruzzini, “An integrated methodology for the assessment of stress and mental workload applied on virtual training,” International Journal of Computer Integrated Manufacturing, vol. 37, no. 1, pp. 78–95, 2024. [Online]. Available: https://www.tandfonline.com/doi/abs/10.1080/0951192X.2023.2189311
M. Gómez-Rios, D. A. Wong Velez, et al., “Design of an Eye Tracking Software Prototype for Improving Human-Computer Interaction,” in Proc. Int. Conf. Applied Human Factors, Springer, 2025. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-031-89760-3_10
A. Brunzini, et al., “Stress and mental workload in virtual training,” Int. J. Comp. Integrated Manuf., vol. 37, no. 1, 2024.
Z. Noor et al., Perancangan Sistem Kerja dan Desain Ergonomi di Industri, Yayasan Kita Menulis, 2025.
I. Hussain, S. Young, and S. J. Park, “Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System,” Sensors, vol. 21, no. 21, p. 6985, 2021. [Online]. Available: https://www.mdpi.com/1424-8220/21/21/6985
P. Keikhosrokiani and I. T. Y. Zhe, “Knowledge Workers’ Mental Workload Prediction Using Optimised ELANFIS,” Applied Intelligence, vol. 51, pp. 771–790, 2021. [Online]. Available: https://link.springer.com/article/10.1007/s10489-020-01928-5
J. Trilar, T. Sobočan, and E. Stojmenova Duh, “Family-Centered Design: Interactive Performance Testing and UI Evaluation of the Slovenian eDavki Portal,” Sensors, vol. 21, no. 15, p. 5161, 2021. [Online]. Available: https://www.mdpi.com/1424-8220/21/15/5161
A. Jolly et al., “Modern Smart Gadgets and Wearables for Diagnosis and Management of Stress,” Healthcare, vol. 13, no. 4, p. 411, 2025. [Online]. Available: https://www.mdpi.com/2227-9032/13/4/411
D. Yoon et al., “Driver Readiness in Autonomous Systems,” Springer Lecture Notes in Computer Science, 2020. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-50732-9_49
N. Dehnen, I. S. MacKenzie, and A. An, “TapFix: Cursorless Typographical Error Correction for Touch-Sensor Displays,” Sensors, vol. 25, no. 5, p. 1421, 2025. [Online]. Available: https://www.mdpi.com/1424-8220/25/5/1421
N. Fürstenau and A. Papenfuss, “Model-based analysis of subjective mental workload during multiple remote tower simulations,” Springer Virtual and Remote Control Tower, 2022. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-93650-1_13
A. Umyati, W. Susihono, and A. S. Mariawati, "Measurement of psychological impact of industrial engineering students in fulfil of online learning outcomes using NASA-TLX method," IOP Conference Series: Materials Science and Engineering, vol. 909, no. 1, p. 012064, 2020. [Online]. Available: https://iopscience.iop.org/article/10.1088/1757-899X/909/1/012064/pdf.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Industrial System Engineering and Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
