Why Riders Break the Rules: A Structural Equation Modeling Approach to Traffic Violations in a Developing Region

Authors

  • Desrina Yusi Irawati Department of Industrial Engineering, Universitas Katolik Darma Cendika, Surabaya, 60117, Indonesia
  • Nyoman Sri Widari Department of Industrial Engineering, Universitas Katolik Darma Cendika, Surabaya, 60117, Indonesia
  • Armadeo Ruben Canariesa Department of Industrial Engineering, Universitas Katolik Darma Cendika, Surabaya, 60117, Indonesia

DOI:

https://doi.org/10.12928/si.v23i2.393

Keywords:

Behavioral intentions, Driving behavior, Traffic violations, TPB, SEM-PLS

Abstract

Traffic violations remain a major contributor to road traffic accidents in Indonesia. Despite government initiatives, limited research has examined the psychological and contextual factors driving this behavior. This study extends the Theory of Planned Behavior (TPB) by incorporating risk perception, habit, emotional condition, environmental condition, and legal knowledge and awareness. A structured questionnaire (N=100) was administered to motorcyclists and/or car drivers in East Java; items were derived from established scales and refined using field observations and a pilot test. Respondents were selected using stratified area sampling to ensure relevance. Data were analyzed using PLS-SEM (SmartPLS). Key findings: attitude and perceived behavioral control significantly predicted behavioral intention; intention strongly predicted actual violation behavior; risk perception negatively predicted permissive attitudes. Habit, subjective norms, emotional and environmental conditions, and legal knowledge were not significant predictors. The study contributes theoretically by refining TPB with risk perception as an antecedent of attitude, and practically by suggesting interventions targeting attitudes and risk awareness supported by technology-assisted enforcement in developing-country contexts.

References

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Arslan, A., Yener, S., & Akturan, A. (2025). Moral identity and the ethics of digital piracy: a self-regulatory model of contextual reasoning andreligious norms. Journal of Information, Communication and Ethics in Society. https://doi.org/10.1108/JICES-05-2025-0097/1301855

Brenner, B. (2024). Entrepreneurial Intentions of South African university students: an application of the theory of planned behaviour. open.uct.ac.za. https://open.uct.ac.za/items/824fffac-9357-44b2-99b7-e60b113ec5b9

Carter, P. M., Bingham, C. R., Zakrajsek, J. S., Shope, J. T., & Sayer, T. B. (2014). Social norms and risk perception: Predictors of distracted driving behavior among novice adolescent drivers. Journal of Adolescent Health, 54(5 SUPPL.), S32–S41. https://doi.org/10.1016/j.jadohealth.2014.01.008

Cheng, Q., Deng, W., & Hu, Q. (2021). Using the Theory of Planned Behavior to Understand Traffic Violation Behaviors in E-Bike Couriers in China. Journal of Advanced Transportation, 2021, 1–11. https://doi.org/10.1155/2021/2427614

Conner, M., & Armitage, J. (1998). Extending the Theory of Planned Behavior: A Review and Avenues for Further Research. Journal of Applied Social Psychology, 28(15), 1429–1464. https://doi.org/10.1111/j.1559-1816.1998.tb01685.x

D’Arco, M., Marino, V., & Resciniti, R. (2025). Exploring the pro-environmental behavioral intention of Generation Z in the tourism context: The role of injunctive social norms and personal norms. Journal of Sustainable Tourism. https://doi.org/10.1080/09669582.2023.2171049

Dapilah, F., Guba, B. Y., & Owusu-Sekyere, E. (2017). Motorcyclist characteristics and traffic behaviour in urban Northern Ghana: Implications for road traffic accidents. Journal of Transport and Health, 4, 237–245. https://doi.org/10.1016/j.jth.2016.03.001

Hai, D. N., Minh, C. C., & Huynh, N. (2024). Meta-analysis of driving behavior studies and assessment of factors using structural equation modeling. International Journal of Transportation Science and Technology, 14, 219–236. https://doi.org/10.1016/j.ijtst.2023.05.002

Hair, J. F., Risher, J. J., & Ringle, C. M. (2018). When to use and how to report the results of PLS-SEM. 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hasan, M. M., Amin, M. Al, Arefin, M. S., & Mostafa, T. (2024). Green consumers’ behavioral intention and loyalty to use mobile organic food delivery applications: The role of social supports, sustainability perceptions, and religious consciousness. In Environment, Development and Sustainability. Springer. https://doi.org/10.1007/s10668-023-03284-z

Henseler. (2017). Partial Least Squares Path Modeling. Advanced Methods for Modeling Markets, 361–381. https://doi.org/10.1007/978-3-319-53469-5

Henseler et al. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. of the Acad. Mark. Sci., 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8

versen, H., & Rundmo, T. (2004). Attitudes towards traffic safety, driving behaviour and accident involvement among the Norwegian public. Ergonomics, 47(5), 555–572. https://doi.org/10.1080/00140130410001658709

Jin, L., Guo, B., Jiang, Y., & Hua, Q. (2021). Analysis on the Influencing Factors of Driving Behaviours Based on Theory of Planned Behaviour. Advances in Civil Engineering, 2021. https://doi.org/10.1155/2021/6687674

Katanararoj, N., Choocharukul, K., & Kunihiro, K. (2024). A comparative study of road traffic violation between Thai and Japanese teenagers. IATSS Research, 48(1), 55–67. https://doi.org/10.1016/j.iatssr.2024.01.001

Li, Z., Man, S. S., Chan, A. H. S., & Zhu, J. (2021). Integration of theory of planned behavior, sensation seeking, and risk perception to explain the risky driving behavior of truck drivers. Sustainability (Switzerland), 13(9), 1–14. https://doi.org/10.3390/su13095214

Liu, Y., Wang, X., & Guo, Y. (2021). The moderating effects of emotions on the relationship between self-reported individual traits and actual risky driving behaviors. Psychology Research and Behavior Management, 14, 423–447. https://doi.org/10.2147/PRBM.S301156

Love, S., Truelove, V., Rowland, B., Kannis-dymand, L., & Davey, J. (2022). Is all high-risk behaviour premeditated? A qualitative exploratory approach to the self-regulation of habitual and risky driving behaviours. Transportation Research Part F: Psychology and Behaviour, 90(August), 312–325. https://doi.org/10.1016/j.trf.2022.09.002

Ng, P. Y., & Phung, P. T. (2021). Public transportation in Hanoi: Applying an integrative model of behavioral intention. Case Studies on Transport Policy, 9(2), 395–404. https://doi.org/10.1016/j.cstp.2020.10.012

Ping, P., Sheng, Y., Qin, W., Miyajima, C., & Takeda, K. (2018). Modeling Driver Risk Perception on City Roads Using Deep Learning. IEEE Access, 6, 68850–68866. https://doi.org/10.1109/ACCESS.2018.2879887

Redaksi, N. (2024). Kecelakaan Lalu Lintas di Indonesia Didominasi Oleh Kendaraan Roda Dua. Korlantas POLRI.

Roche, S. P., Wilson, T., & Pickett, J. T. (2020). Perceived Control, Severity, Certainty, and Emotional Fear: Testing an Expanded Model of Deterrence. Journal of Research in Crime and Delinquency, 57(4), 493–531. https://doi.org/10.1177/0022427819888249

Sadia, R., Bekhor, S., & Polus, A. (2018). Structural equations modelling of drivers’ speed selection using environmental, driver, and risk factors. Accident Analysis and Prevention, 116(July 2017), 21–29. https://doi.org/10.1016/j.aap.2017.08.034

Sayed, I., Abdelgawad, H., Said, D., Mazengia, E. M., Kassie, A., Zewdie, A., Demissie, G. D., Schlesinger, L. E., Safren, M. A., Signing, R., Zhang, X., Chang, R., Wang, M., & Sui, X. (2023). Perceptual Analysis of the Driving Task. BMC Public Health, 23(1), 54–61.

Scott-Parker, B., Watson, B., King, M. J., & Hyde, M. K. (2014). “I Drove After Drinking Alcohol” and Other Risky Driving Behaviours Reported By Young Novice Drivers. Accident Analysis and Prevention, 70, 65–73. https://doi.org/10.1016/j.aap.2014.03.002

Seefong, M., Wisutwattanasak, P., Se, C., Theerathitichaipa, K., Jomnonkwao, S., Champahom, T., Ratanavaraha, V., & Kasemsri, R. (2024). A study of motorcycle riders related to speeding behavior in Thailand’s Industrial zones. Scientific Reports, 14(1), 1–14. https://doi.org/10.1038/s41598-024-81793-1

Tang, X., Yuan, Z., & Qu, S. (2025). Factors Influencing University Students’ Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.13105

Teye-Kwadjo, E. (2019). Risky driving behaviour in urban Ghana: the contributions of fatalistic beliefs, risk perception, and risk-taking attitude. International Journal of Health Promotion and Education, 57(5), 256–273. https://doi.org/10.1080/14635240.2019.1613163

Vinh, D., Nguyen, M., Tuan, A., Ross, V., Brijs, T., Wets, G., & Brijs, K. (2022). Small-displacement motorcycle crashes and risky ridership in Vietnam: Findings from a focus group and in-depth interview study. Safety Science, 152(September 2021), 105514. https://doi.org/10.1016/j.ssci.2021.105514

Wang, X., & Xu, L. (2024). Factors influencing drivers’ queue-jumping behavior at urban intersections: A covariance-based structural equation modeling analysis. Electronic Research Archive, 32(3), 1439–1470. https://doi.org/10.3934/ERA.2024067

Wegman, F. (2017). The future of road safety: A worldwide perspective. IATSS Research, 40(2), 66–71. https://doi.org/10.1016/j.iatssr.2016.05.003

WHO. (2023). Road traffic injuries. World Health Organization.

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Published

2025-10-31

How to Cite

Yusi Irawati, D., Sri Widari , N., & Ruben Canariesa , A. . (2025). Why Riders Break the Rules: A Structural Equation Modeling Approach to Traffic Violations in a Developing Region. Spektrum Industri, 23(2), 249–262. https://doi.org/10.12928/si.v23i2.393

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Section

Industrial Management and Entrepreneurship