IT Project Governance Maturity as a Predictor of Delivery Performance in Public Universities
DOI:
https://doi.org/10.51903/jmi.v5i1.340Keywords:
Delivery Performance, Governance Maturity, Higher Education, IT Project Management, Public UniversitiesAbstract
his study examines the predictive role of IT project governance maturity on delivery performance in public universities, a context that has received limited empirical attention. Despite the critical importance of governance in higher education IT projects, few studies have quantitatively explored how maturity levels influence cost, schedule, and quality outcomes. The research objective is to analyze the extent to which governance maturity predicts delivery performance, providing a conceptual and empirical foundation for optimizing IT project outcomes. A non-experimental, quantitative approach was adopted, primarily simulation-based data designed to approximate institutional project conditions, incorporating variables such as project size, complexity, and performance indicators. Governance maturity was assessed using established frameworks such as COBIT and PMMM, while inferential analysis employed multiple regression and path analysis to evaluate predictive relationships. The findings indicate that governance maturity significantly predicts delivery performance, with higher maturity levels associated with improved project outcomes, while project complexity negatively affects performance in institutions with lower governance capability. Project size showed no significant effect, highlighting governance quality as the primary determinant of delivery success. These results offer practical implications for university IT managers, suggesting that investment in structured policies, formal monitoring mechanisms, and clear decision authority can enhance project outcomes. The study provides a preliminary predictive model that can support data-informed decision-making and serve as a reference point for future research in higher education IT governance. Findings should be interpreted as exploratory due to the use of simulation-based data.References
Aaltonen, K., & Turkulainen, V. (2022). Institutionalization of a Collaborative Governance Model to Deliver Large, Inter-Organizational Projects. International Journal of Operations and Production Management, 42(8), 1294–1328. https://doi.org/10.1108/ijopm-11-2021-0741
Alghizzawi, M., Ahmed, E., Al-Gasawneh, J. A., & Alhawamdeh, Z. M. (2024). Corporate Governance Paradigm in Developing Country: Theoretical Overview. In Studies in Systems, Decision and Control, 897–907. https://doi.org/10.1007/978-3-031-54383-8_68
Anwar, D. M., Emita, I., Melyani, M., Rahadjeng, I. R., Indrarti, W., Rafik, A., & Sari, D. I. (2026). Optimizing Regional Financial Management through the Transformation of the Digital Financial Information System in the Bekasi City Government. Journal of Technology Informatics and Engineering, 5(1), 200–218. https://doi.org/10.51903/jtie.v5i1.495
Archaqie, H. N. R., & Pratiwi, P. (2025). Assessing IT Governance in Digital UMKM Application Projects: A Comparative Study Using the COBIT 2019 Framework. Jurnal Ilmiah Sistem Informasi, 4(2), 232–241. https://doi.org/10.51903/wpt7q648
Bondarenko, S., Halachenko, O., Shmorgun, L., Volokhova, I., Khomutenko, A., & Krainov, V. (2021). The Effectiveness of Network Systems in Providing Project Maturity of Public Management. TEM Journal, 10(1), 272–282. https://doi.org/10.18421/tem101-34
Boselie, P., Van Harten, J., & Veld, M. (2021). A Human Resource Management Review on Public Management and Public Administration Research: Stop Right There… Before We Go Any Further…. Public Management Review, 23(4), 483–500. https://doi.org/10.1080/14719037.2019.1695880
Bryson, J. M., George, B., & Seo, D. (2024). Understanding Goal Formation in Strategic Public Management: A Proposed Theoretical Framework. Public Management Review, 26(2), 539–564. https://doi.org/10.1080/14719037.2022.2103173
De Ramón Fernández, A., Ruiz Fernández, D., & Prieto Sánchez, M. T. (2022). Prediction of the Mode of Delivery Using Artificial Intelligence Algorithms. Computer Methods and Programs in Biomedicine, 219, 106740. https://doi.org/10.1016/j.cmpb.2022.106740
Farouq, A., & Rios, C. (2025). The Role of Strategic Financial Planning in Enhancing Organizational Resilience: A Cross-Industry Perspective. Journal of Management and Informatics, 4(3), 947–962. https://doi.org/10.51903/jmi.v4i3.301
Fesenko, G., Fesenko, T., Fesenko, H., Shakhov, A., Yakunin, A., & Korzhenko, V. (2021). Developing E-Maturity Model for Municipal Project and Program Management System. Eastern-European Journal of Enterprise Technologies, 1(3), 15–28. https://doi.org/10.15587/1729-4061.2021.225278
Gao, C., Zhang, F., Wu, G., Hu, Q., Ru, Q., Hao, J., He, R., & Sun, Z. (2021). A Deep Learning Method for Route and Time Prediction in Food Delivery Service. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2879–2889. https://doi.org/10.1145/3447548.3467068
Hansen, J. R., Pop, M., Skov, M. B., & George, B. (2024). A Review of Open Strategy: Bridging Strategy and Public Management Research. Public Management Review, 26(3), 678–700. https://doi.org/10.1080/14719037.2022.2116091
Hikmah, N., Fauzi, A., & Nayyiroh, F. U. (2025). Measuring the Forecast Accuracy in Retail MSMEs: A Comparative Analysis between AI and Traditional Methods in the Era of Digital Selling. Journal of Management and Informatics, 4(1), 687–705. https://doi.org/10.51903/jmi.v4i1.166
Joshi, A., Ruiz, L., & De Haes, S. (2021). Impact of IT Governance Process Capability on Business Performance: Theory and Empirical Evidence. International Journal of IT/Business Alignment and Governance (IJITBAG), 12(1), 1–19. https://doi.org/10.4018/ijitbag.2021010101
Knies, E., Boselie, P., Gould-Williams, J., & Vandenabeele, W. (2024). Strategic Human Resource Management and Public Sector Performance: Context Matters. International Journal of Human Resource Management, 35(14), 2432–2444. https://doi.org/10.1080/09585192.2017.1407088
Levstek, A., Pucihar, A., & Hovelja, T. (2022). Towards an Adaptive Strategic IT Governance Model for SMEs. Journal of Theoretical and Applied Electronic Commerce Research, 17(1), 230–252. https://doi.org/10.3390/jtaer17010012
Liu, S., He, L., & Shen, Z. J. M. (2021). On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors. Management Science, 67(7), 4095–4119. https://doi.org/10.1287/mnsc.2020.3741
Mai, N. T., & Khalid, I. (2025). Human Error vs. System Security: Evaluating the Weakest Link in Digital Business Information Systems. Journal of Management and Informatics, 4(3), 981–997. https://doi.org/10.51903/jmi.v4i3.305
Mäntymäki, M., Minkkinen, M., Birkstedt, T., & Viljanen, M. (2022). Defining Organizational AI Governance. AI and Ethics, 2(4), 603–609. https://doi.org/10.1007/s43681-022-00143-x
Marcelino, P., de Lurdes Antunes, M., Fortunato, E., & Gomes, M. C. (2021). Machine Learning Approach for Pavement Performance Prediction. International Journal of Pavement Engineering, 22(3), 341–354. https://doi.org/10.1080/10298436.2019.1609673
Parker, L., Martin-Sardesai, A., & Guthrie, J. (2023). The Commercialized Australian Public University: An Accountingized Transition. Financial Accountability and Management, 39(1), 125–150. https://doi.org/10.1111/faam.12310
Pertiwi, J. P., & Hana, A. U. (2025). Data-Driven Decision Making in MSMEs: Leveraging Free Analytics Tools for Financial Planning and Efficiency. Journal of Management and Informatics, 4(1), 633–648. https://doi.org/10.51903/jmi.v4i1.146
Ramadhani, D. P. S., Sulaiman, H. R., Anggraeni, A. W., & Aisyah, S. (2025). The Effectiveness of E-Government Services in Enhancing Public Trust: A Comparative Study Across ASEAN Countries. Journal of Management and Informatics, 4(1), 649–667. https://doi.org/10.51903/jmi.v4i1.150
Sahu, S. K., Mokhade, A., & Bokde, N. D. (2023). An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges. Applied Sciences, 13(3), 1956. https://doi.org/10.3390/app13031956
Stepan, H., Galindo, A., Hund, M., Schlembach, D., Sillman, J., Surbek, D., & Vatish, M. (2023). Clinical Utility of sFlt-1 and PlGF in Screening, Prediction, Diagnosis and Monitoring of Pre-Eclampsia and Fetal Growth Restriction. Ultrasound in Obstetrics and Gynecology, 61(2), 168–180. https://doi.org/10.1002/uog.26032
Wibisono, G., Nikhlis, N., Wicaksono, Y. A., & Faradila, S. (2025). Enhancing Decision Quality and Transparency via Machine Learning-Based Goodwill Impairment Estimation in Banks. Journal of Management and Informatics, 4(3), 1059–1074. https://doi.org/10.51903/jmi.v4i3.233
Willie, M. M. (2025). Value-Based Administration Services and Value-Based Care: Aligning Administrative Efficiency with Patient Outcomes. Journal of Management and Informatics, 4(3), 1032–1042. https://doi.org/10.51903/jmi.v4i3.308
Wolter, J., & Hanne, T. (2024). Prediction of Service Time for Home Delivery Services Using Machine Learning. Soft Computing, 28(6), 5045–5056. https://doi.org/10.1007/s00500-023-09220-7
Zhang, Y., Yun, Y., An, R., Cui, J., Dai, H., & Shang, X. (2021). Educational Data Mining Techniques for Student Performance Prediction: Method Review and Comparison Analysis. Frontiers in Psychology, 12, 698490. https://doi.org/10.3389/fpsyg.2021.698490
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