Customer Acceptance of Driver State Monitoring Systems in Malaysia: A Technology Acceptance Model Perspective
Authors
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka, Melaka (Malaysia)
Fakulti Teknologi dan Kejuruteraan Mekanikal, Universiti Teknikal Malaysia Melaka, Melaka (Malaysia)
Fakulti Teknologi dan Kejuruteraan Mekanikal, Universiti Teknikal Malaysia Melaka, Melaka; and Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505 (Malaysia, Japan)
Article Information
DOI: 10.47772/IJRISS.2025.92800026
Subject Category: Technology
Volume/Issue: 9/28 | Page No: 272-280
Publication Timeline
Submitted: 2025-11-10
Accepted: 2025-11-22
Published: 2025-12-19
Abstract
Driver State Monitoring Systems (DSMS) are high-tech in-car systems that are designed to improve the safety of the road by alerting the driver to fatigue, distraction, and unsafe driving. Despite the growing international use of DSMS, the adoption among users in Malaysia is still not empirically studied yet, although road safety issues in the country continue to be a challenge. This paper uses the Technology Acceptance Model (TAM) to test the acceptance of Malaysian drivers toward the use of DSMS. An online survey was used to gather 564 licensed drivers’ data which was analyzed using Structural Equation Modeling (SEM). Findings suggest that the perceived usefulness (PU) has a greater impact on the attitudes towards DSMS, as opposed to the perceived ease of use (PEOU). Attitude in its turn is a major predictor of purchase intention, which highlights its key influence on the adoption behavior. These results indicate that it is important to emphasize the usefulness of DSMS to build acceptance in Malaysia. Further research must be carried out to expand this study, to incorporate cultural, behavioral, and ethical aspects of the adoption of DSMS.
Keywords
Driver State Monitoring Systems (DSMS),
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References
1. Samsuddin, N., & Mohd Masirin, M. I. (2016). Assessment of Road Infrastructures Pertaining to Malaysian Experience. https://doi.org/10.1051/MATECCONF/20164703010 [Google Scholar] [Crossref]
2. Nirmali, B., Wickramasinghe, S., Munasinghe, T., Amalraj, C. R. J., & Bandara, H. M. N. D. (2017, December 1). Vehicular data acquisition and analytics system for real-time driver behavior monitoring and anomaly detection. International Conference on Industrial and Information Systems. https://doi.org/10.1109/ICIINFS.2017.8300417 [Google Scholar] [Crossref]
3. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management. https://doi.org/10.1016/S0378-7206(01)00143-4 [Google Scholar] [Crossref]
4. Abdul Razak, S. F., Yogarayan, S., Azman, A., Abdullah, M. F. A., Muhamad Amin, A. H., & Salleh, M. (2021). Driver perceptions of advanced driver assistance systems: A case study. F1000Research. https://doi.org/10.12688/F1000RESEARCH.73400.1 [Google Scholar] [Crossref]
5. Sharshir, S. W., Yuan, Z., Elsharkawy, M., Hamada, M. A., Swidan, A., Abdelaziz, G. B., Abdullah, A. S., & El-Samadony, M. O. A. (2023). Performance investigation of a tubular distiller using parabolic concentrator with various modifications. https://doi.org/10.1016/j.psep.2023.09.024 [Google Scholar] [Crossref]
6. Moshayedi, J., Nowzari, R., Taherinezhad, M., Najafabadi, H. E., Khan, A. S., Ghadiri Nejad, M., & Ghanbari, N. (2025). Investigation and Characterization of Pipe Defects and Techniques, and Challenges Toward the Protection of Environmental Protection. Iranian Journal of Chemistry and Chemical Engineering. https://doi.org/10.30492/ijcce.2025.2051834.6993 [Google Scholar] [Crossref]
7. Choi, J., & Ji, Y. (2015). Understanding the acceptance of driver assistance technologies: A technology acceptance model perspective. Transportation Research Part F: Traffic Psychology and Behaviour, 30, 1–12. [Google Scholar] [Crossref]
8. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. [Google Scholar] [Crossref]
9. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. [Google Scholar] [Crossref]
10. Bansal, P., Kockelman, K. M., & Singh, A. (2016). Assessing public acceptance of autonomous vehicles: A case study of Austin, Texas. Transportation Research Record, 2564(1), 1–9. [Google Scholar] [Crossref]
11. Malaysian Institute of Road Safety Research (MIROS). (2022). Road Safety Research. [Source link to be inserted]. [Google Scholar] [Crossref]
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