A System dynamics quantitative model for enhancing e-government maturity in the indonesian education sector
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Abstract
This study develops a deterministic mathematical model integrated with system dynamics to measure key success factors driving e-government maturity in Indonesia’s education sector. Addressing the gap in previous research, which mainly relied on descriptive methods, the model quantitatively examines causal relationships among leadership commitment, budget support, digital infrastructure, human capital, service quality, and feedback mechanisms. The methodology involves three stages: (1) constructing a causal loop diagram based on theoretical and empirical insights, (2) converting these relationships into a linear system of equations normalized on a [0–1] scale, and (3) performing simulations and sensitivity analyses to evaluate policy scenarios. Simulation results indicate that even relatively high leadership commitment (K=0.75) only produces moderate maturity levels (M≈0.409). The greatest improvement occurs when feedback loops are reinforced and service quality investments are prioritized. Sensitivity analysis reveals the model is particularly responsive to changes in feedback effectiveness and service quality weighting, identifying these as critical leverage points for accelerating transformation. Under optimal conditions, maturity can increase from 0.41 to 0.48, reflecting a 7% gain over the baseline. The study contributes a replicable quantitative framework for evidence-based policymaking, while noting limitations in parameter assumptions and empirical calibration for future refinement.
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How to Cite
Yulistiawan, B. S., Widyastuti, R., Mulianingtyas, R. O., A, G. P. R., & Sihotang, H. T. (2025). A System dynamics quantitative model for enhancing e-government maturity in the indonesian education sector. International Journal of Basic and Applied Science, 14(2), 70–84. https://doi.org/10.35335/ijobas.v14i2.693
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F. B. I. N. B. NASUTION, “Integrated double loop data driven model for public policymaking,” 2019, Universiti Teknologi Malaysia. [Online]. Available: https://core.ac.uk/download/pdf/543494355.pdf
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G. Gunadi, “A formal model of e-government success factors for developing countries: dynamic relationships of software maintenance and information systems staff management,” 2012, Victoria University. [Online]. Available: https://vuir.vu.edu.au/42055/
K.-K. Tung and K. K. Tung, Topics in mathematical modeling, vol. 10. Princeton University Press Princeton, 2007. [Online]. Available: https://austms.org.au/wp-content/uploads/Gazette/2008/Mar08/BookReviews.pdf
S. Banerjee, Mathematical modeling: models, analysis and applications. Chapman and Hall/CRC, 2021. doi: https://doi.org/10.1201/9781351022941.
H. Balouei Jamkhaneh, R. Shahin, and A. Shahin, “Assessing sustainable tourism development through service supply chain process maturity and service quality model,” Int. J. Product. Perform. Manag., vol. 72, no. 7, pp. 2046–2068, 2023, doi: https://doi.org/10.1108/IJPPM-07-2021-0437.
M. A. Mohamed Hashim, I. Tlemsani, and R. Matthews, “Higher education strategy in digital transformation,” Educ. Inf. Technol., vol. 27, no. 3, pp. 3171–3195, 2022, doi: https://doi.org/10.1007/s10639-021-10739-1.
V. Đurek, N. Kadoic, and N. B. Ređep, “Assessing the digital maturity level of higher education institutions,” in 2018 41st international convention on information and communication technology, electronics and microelectronics (MIPRO), IEEE, 2018, pp. 671–676. doi: https://doi.org/10.23919/MIPRO.2018.8400126.
ISACA, “COBIT 5 Framework,” ISACA. [Online]. Available: https://www.isaca.org/resources/cobit
A. Sysoev, “Sensitivity analysis of mathematical models,” Computation, vol. 11, no. 8, p. 159, 2023, doi: https://doi.org/10.3390/computation11080159.
L. Arriola and J. M. Hyman, “Sensitivity analysis for uncertainty quantification in mathematical models,” in Mathematical and statistical estimation approaches in epidemiology, Springer, 2009, pp. 195–247. doi: https://doi.org/10.1007/978-90-481-2313-1_10.
P. S. Calabro, G. Mannina, and G. Viviani, “In sewer processes: mathematical model development and sensitivity analysis,” Water Sci. Technol., vol. 60, no. 1, pp. 107–115, 2009, doi: https://doi.org/10.2166/wst.2009.296.
M. Silberbush and S. A. Barber, “Sensitivity analysis of parameters used in simulating K uptake with a mechanistic mathematical model 1,” Agron. J., vol. 75, no. 6, pp. 851–854, 1983, doi: https://doi.org/10.2134/agronj1983.00021962007500060002x.
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