Effect of Fuel Management Information Systems (FMIS) on the Operational Performance of Government Vehicles in Tanzania

Mwinyi Mohamed; Angela-Aida K.  Runyoro

doi:10.47667/ijpasr.v6i2.408

Egileak

Mwinyi Mohamed Faculty of Logistics and Business Studies, National Institute of Transport, P. O Box 705, Dar es Salaam-Tanzania
Angela-Aida K. Runyoro Head, Computing and Communication Technology, National Institute of Transport, P. O Box 705, Dar es Salaam-Tanzania

##plugins.pubIds.doi.readerDisplayName##:

https://doi.org/10.47667/ijpasr.v6i2.408

Gako-hitzak:

Fuel Management Systems, Fleet Tracking, Government Vehicles, Operational Performance, Tanzania

Laburpena

The study examines the effects of Fuel Management Information Systems (FMIS) on the operational performance of government vehicles in Tanzania, focusing on digital tracking, fuel monitoring, and digital reporting. Conducted at the Ministry of Minerals headquarters in Dodoma, it adopted a pragmatism philosophy, a mixed-methods approach, and a cross-sectional design. A sample of 62 respondents, including drivers, transport officers, IT staff, procurement staff, and departmental heads, was selected through random and purposive sampling. Quantitative analysis employed multiple regression techniques. Findings revealed that digital tracking had a positive and significant effect on operational performance (β=0.387, p=0.000), which increased with individual moderators but became insignificant when combined. Fuel monitoring showed a strong positive and significant effect (β=0.595, p=0.000), remaining significant after individual moderation but turning negative under combined moderators (β=-0.225, p=0.020). Digital reporting had a positive but insignificant effect (β=0.016, p=0.795), which remained insignificant after individual moderation but became significantly negative under combined moderators (β=-0.896, p=0.024). The study recommends institutionalizing GPS-based tracking in all government vehicles, conducting training for drivers and fleet managers, and establishing robust data governance policies to secure information and enhance trust in FMIS usage.

##plugins.generic.usageStats.downloads##

##plugins.generic.usageStats.noStats##

Erreferentziak

Abedias, H., Ansari, A., Hosseini, V., Koch, C. R., & Shahbakhti, M. (2023). Real-time vehicular fuel consumption estimation using machine learning and on-board diagnostics data. Journal of Automobile Engineering, 238(12), 3779–3793.

Ansari, M. A. (2024). GPS based vehicle-tracking system. International Journal of Research Publications and Reviews, 5(7), 388–391.

Ben Halima, N., El Kamel, A., & Ksouri, M. (2021). Modeling and optimization of energy consumption in transport fleets using digital tracking systems. Energy Reports, 7, 198–207.

Blaikie, N., & Priest, J. (2019). Designing social research (3rd ed.). Polity.

Boyle, M. P., & Schmierbach, M. (2019). Applied communication research methods: Getting started as a researcher. Routledge.

Controller and Auditor General (CAG). (2022). Performance audit on the management of government vehicles maintenance. United Republic of Tanzania.

Chatterjee, S. N. P., Rana, Y. K. D., & Abdullah, M. B. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170, 12–38.

Chiparo, J. P., Tukuta, M., & Musanzikwa, M. (2022). Vehicle fleet management practices and service delivery in state enterprises in Zimbabwe. Journal of Transport Technology, 12, 159–171.

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approach (5th ed.). Sage.

Easy Track. (2023). The power of vehicle tracking system in Tanzania.

Hassan, R., & Mahmoud, S. (2020). Optimizing routes for public vehicles to save fuel: Software solutions in government fleets. International Journal of Urban Transport, 6(3), 115–134.

Kanarachos, S., Nathanail, E., & Kotsialos, A. (2020). Real-time vehicle emissions and fuel consumption modeling using telematics. Transportation Research Part D: Transport and Environment, 85, 102398.

Kanuku, S., & Ng’eno, W. K. (2023). Technology application effects on maintenance efficiency of the fleet at the Parliamentary Service Commission, Kenya. International Academic Journal of Arts and Humanities, 1(3), 422–438.

Kennedy, B. L., & Thornberg, R. (2018). Deduction, induction and abduction. In U. Flick (Ed.), The Sage handbook of qualitative data collection (pp. 49–64). Sage.

Khatun, F., Rahman, M., & Sultana, N. (2021). Design and implementation of a real-time vehicle fuel monitoring system. Journal of Sensor and Actuator Networks, 10(2), 29.

Khin, S., & Oo, A. M. T. (2020). The role of IoT-based fuel monitoring systems in improving fleet performance. International Journal of Intelligent Transportation Systems, 8(2), 55–64.

Kothari, R. C. (2019). Research methodology: Methods and techniques (4th ed.). New Age International.

Koranteng, S., & Nyarko, A. (2021). Assessing the success and challenges of the manual vehicle logging system on fleet management: A case study of the Ghana Civil Service.

Lincoln, Y. S., Lynham, S. A., & Guba, E. G. (2018). Paradigmatic controversies, contradictions and emerging confluences revisited. In U. Flick (Ed.), The Sage handbook of qualitative research (5th ed., pp. 108–150). Sage.

Maduka, O. C., & Ibrahim, M. (2023). A mini review of GPS-GSM vehicle tracking systems with microcontroller applications. International Journal of Embedded Systems Applications, 11(4), 17–25.

Moawad, A., Kim, N., & Rousseau, A. (2024). Simulation-based assessment of fuel economy in connected and automated vehicles. Transportation Science, 58(2), 90–106.

Mwenisongole, C. L. (2024). The use of natural gas in operating fuel vehicles as an alternative technology in Tanzania. International Journal of Scientific and Engineering Research, 12(3), 1–13.

National Academies of Sciences, Engineering, and Medicine. (2021). Assessment of technologies for improving light-duty vehicle fuel economy 2025–2035. The National Academies Press.

Pimentel, J. L. (2010). A note on the usage of Likert scaling for research data analysis. USM R&D Journal, 18(2), 109–112.

Ping, Y., Liu, H., & Zhang, Y. (2021). IoT-enabled fuel management systems for intelligent fleet operations. IEEE Internet of Things Journal, 8(5), 3921–3932.

Sanya, K. (2021). Introduction to research philosophy. Journal of Anthropological and Archaeological Sciences, 5(4), 653–661.

Šarkan, B., Gašparík, J., & Stopka, O. (2022). Application of smart technologies in fuel tracking for freight rail operations. Communications - Scientific Letters of the University of Žilina, 24(3), 67–74.

Šarkan, B., Gašparík, J., & Stopka, O. (2025). Efficiency analysis of vehicle tracking and fuel control systems in public logistics. Transport and Communications Journal, 21(1), 44–53.

Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson.

Shanthi, A., Adnan, A. A., Jamil, N. I., Rosle, A. N., & Shafrminnie, E. (2021). Exploring university students’ acceptance of open distance learning using TAM. International Journal of Academic Research, 11(10), 250–262.

Srinagesh, D., Ramesh, V., & Kumar, S. (2025). Impact of fuel tracking technologies on public transport efficiency. Journal of Transport Research and Applications, 15(1), 22–35.

Saini, M., Gupta, R., & Arora, P. (2020). Evaluation of GPS and GSM-based vehicle tracking system using Arduino platform. International Journal of Engineering Research and Technology, 9(6), 1130–1135.

Sun, Q., Li, J., & Zhao, W. (2021). Fuel consumption prediction for urban fleet vehicles based on deep learning. Applied Energy, 289, 116658.