Effectiveness of Elementary School Management through Digital Literacy, Infrastructure Readiness, Hybrid-Based Organizational Culture (Integration of Artificial Intelligence and Data-Driven Decision Making): An Empirical Study
Abstract
This paper explored the effectiveness of elementary school management based on a hybrid model that means fusing data driven decision making with artificial intelligence in government elementary schools in the Gorontalo Regency of Indonesia. The study utilized a quantitative explanatory research design to explore the impact of digital literacy, infrastructure preparedness, and organization culture on the effectiveness of school management. The sample size consisted of principals, teachers, and administrative personnel and 205 respondents were chosen by proportional stratified random sampling. A Likert scale questionnaire was used to gather data and Structural Equation Modeling with Partial Least Squares was used to analyze the data. The results found that digital literacy, infrastructure preparedness, and organizational culture had a positive impact on management effectiveness. The strongest predictor was found to be digital literacy and then organizational culture and infrastructure readiness became the predictors. These three predictors had a high explanatory power, and the value of R square was 0.714, which showed that three predictors explained 71.4 percent of the variance in the effectiveness of management in schools. These findings imply that effective deployment of hybrid data based and AI supported school management does not merely require the presence of technology but also the ability of school actors to comprehend data, cooperate and maintain adaptable organizational behavior. This research concludes that the promotion of effective and context sensitive digital transformation in the management of elementary schools must be made priorities by enhancing digital literacy and cultivating a positive organizational culture.
References
Abduh, A., Achmad, S. F., Syam, C., Samad, S., Arham, M., Mustafa, M., & Pandang, A. (2026). Dataset on the number of schools, teachers, and students in Sulawesi, Indonesia: kindergarten, primary, junior, senior high, vocational, and Islamic boarding schools with educational access, quality, and cultural implications to solve challenges and strategies in education management and support Sustainable Development Goals (SDGs). ASEAN Journal of Educational Research and Technology, 5(1), 89-116.
Adeleke, A. A., Orunbon, N. O., Shahid, A., Mowafaq, F., & Majid, A. (2024). Revolutionizing Education Harnessing Technology For Efficient School Management. Library of Progress-Library Science, Information Technology & Computer, 44(3). https://doi.org/10.48165/bapas.2024.44.2.1
Aithal, P. S., & Maiya, A. K. (2023). Innovations in higher education industry–Shaping the future. International Journal of Case Studies in Business, IT, and Education (IJCSBE), 7(4), 283-311. https://doi.org/10.2139/ssrn.4770797
Akimov, N., Kurmanov, N., Uskelenova, A., Aidargaliyeva, N., Mukhiyayeva, D., Rakhimova, S., ... & Utegenova, Z. (2023). Components of education 4.0 in open innovation competence frameworks: Systematic review. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), 100037. https://doi.org/10.1016/j.joitmc.2023.100037
Alenezi, M. (2023). Digital learning and digital institution in higher education. Education Sciences, 13(1), 88. https://doi.org/10.3390/educsci13010088
AlShammari, I., & AlAjmi, M. (2025). School principals’ perspectives on applying data-driven decision making DDDM in centralized school settings. The Asia-Pacific Education Researcher, 34(2), 605-615. https://doi.org/10.1007/s40299-024-00882-x
Barile, D., Secundo, G., & Del Vecchio, P. (2026). An artificial intelligence-based innovation ecosystem enabling open innovation and sustainable growth: evidence from a case study. Innovation, 28(1), 14-36. https://doi.org/10.1080/14479338.2025.2514468
Berry, B. (Ed.). (2011). Teaching 2030: What we must do for our students and our public schools: Now and in the future. Teachers College Press.
Chen, X., Zou, D., Cheng, G., & Xie, H. (2021). Artificial intelligence in education: A review. Computers and Education: Artificial Intelligence, 2, 100011. https://doi.org/10.1016/j.caeai.2021.100011
Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE.
Dai, R., Thomas, M. K. E., & Rawolle, S. (2025). The roles of AI and educational leaders in AI-assisted administrative decision-making: a proposed framework for symbiotic collaboration. The Australian Educational Researcher, 52(2), 1471-1487. https://doi.org/10.1007/s13384-024-00771-8
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
DeMatthews, D. E., & Wang, Y. (2023). How can principals lead in the school improvement planning process? Reducing biases in shared decision making. The Clearing house: a Journal of eduCaTional sTraTegies, issues and ideas, 96(2), 43-51. https://doi.org/10.1080/00098655.2022.2163971
Esomonu, N. P. M. (2025). Utilizing AI and big data for predictive insights on institutional performance and student success: a data-driven approach to quality assurance. AI and ethics, academic integrity and the future of quality assurance in higher education, 29.
Evenstein Sigalov, S., Hershkovitz, A., Cohen, A., & Nachmias, R. (2025). Trusting the data: an updated framework for teachers’ data-driven decision-making (DDDM) in higher education. Education and Information Technologies, 1-29. https://doi.org/10.1007/s10639-025-13819-8
Gonugunta, K. C., & Leo, K. (2024). Role of data-driven decision making in enhancing higher education performance: A comprehensive analysis of analytics in institutional management. International Journal of Acta Informatica, 3(1), 149-159.
Gurram, N. T., Narender, M., Bhardwaj, S., & Kalita, J. P. (2025). A Hybrid Framework for Smart Educational Governance Using AI, Blockchain, and Data-Driven Management Systems. Advances in Consumer Research, 2(5).
Gurram, N. T., Narender, M., Bhardwaj, S., & Kalita, J. P. (2025). A Hybrid Framework for Smart Educational Governance Using AI, Blockchain, and Data-Driven Management Systems. Advances in Consumer Research, 2(5).
Gurram, N. T., Narender, M., Bhardwaj, S., & Kalita, J. P. (2025). A Hybrid Framework for Smart Educational Governance Using AI, Blockchain, and Data-Driven Management Systems. Advances in Consumer Research, 2(5).
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2019). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE.
Hamdanah, H. (2025). Digital Islamic Education Management: Elementary School Principals' Strategies in Technology-Based Learning and Administration Transformation. Auladuna: Jurnal Prodi Pendidikan Guru Madrasah Ibtidaiyah, 7(02), 276-293. https://doi.org/10.62097/ad.v7i02.2713
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Katz, D., & Kahn, R. L. (1978). The Social Psychology of Organizations. Wiley. https://doi.org/10.1177/105960117800300104
Khofi, M. B., & Santoso, S. (2024). Optimize the Role of The State Islamic High School (MAN) Bondowoso Principal in Promoting Digital-Based Learning. JERIT: Journal of Educational Research and Innovation Technology, 1(2), 91-102. https://doi.org/10.34125/jerit.v1i2.7
Koukaras, C., Hatzikraniotis, E., Mitsiaki, M., Koukaras, P., Tjortjis, C., & Stavrinides, S. G. (2025). Revolutionising educational management with AI and wireless networks: a framework for smart resource allocation and decision-making. Applied Sciences, 15(10), 5293. https://doi.org/10.3390/app15105293
Kovalenko, M., Lomonosova, O., & Rusnak, A. (2021). Strategies and technologies of adaptive management of higher education institutions in a rapidly changing external environment. Baltic Journal of Economic Studies, 7(2), 118-128. https://doi.org/10.30525/2256-0742/2021-7-2-118-128
Mamun, M. N. H. (2025). Role of AI and Data Science in Data-Driven DecisionMaking for it Business Intelligence: A Systematic Literature Review. Available at SSRN 5402976.
Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education. RAND Corporation.
Mhlungu, N. S., Chen, J. Y., & Alkema, P. (2019). The underlying factors of a successful organisational digital transformation. South African journal of information management, 21(1), 1-10. https://doi.org/10.4102/sajim.v21i1.995
Munagandla, V. B., Dandyala, S. S. V., & Vadde, B. C. (2024). Improving educational outcomes through data-driven decision-making. International Journal of Advanced Engineering Technologies and Innovations, 3(1), 698-718.
Okokoyo, I. E., Nwaham, C. O., & Nwachukwu, O. G. (2024). Leveraging artificial intelligence for enhanced administrators decision making in educational institutions: A comprehensive exploration of applications, challenges, and opportunities. NIU Journal of Educational Research, 10(1), 63-72. https://doi.org/10.58709/niujed.v10i1.1937
Qudrat-Ullah, H. (Ed.). (2024). Empowering educational leaders using analytics, AI, and systems thinking. IGI Global.
Rahman, M. M. (2024). Data-Driven Decision-Making Through Customer Relationship Management: A Systematic Literature Review In Modern Enterprises. American Journal of Advanced Technology and Engineering Solutions, 4(03), 30-59. https://doi.org/10.63125/vgr9th76
Reigeluth, C. M., & Karnopp, J. R. (2013). Reinventing Schools: It's Time to Break the Mold. Rowman & Littlefield.
Schein, E. H. (2010). Organizational Culture and Leadership (4th ed.). Jossey-Bass. https://doi.org/10.1002/9781119206453
Shwedeh, F. (2024). The integration of artificial intelligence (AI) into decision support systems within higher education institutions. Nanotechnology Perceptions, 20(5), 331-357.
Sunarjo, R. A., Chakim, M. H. R., Maulana, S., & Fitriani, G. (2024). Management of educational institutions through information systems for enhanced efficiency and decision-making. International Transactions on Education Technology (ITEE), 3(1), 47-61. https://doi.org/10.33050/itee.v3i1.670
Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205. https://doi.org/10.1016/j.csda.2004.03.005
Timotheou, S., Miliou, O., Dimitriadis, Y., Sobrino, S. V., Giannoutsou, N., Cachia, R., ... & Ioannou, A. (2023). Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review. Education and information technologies, 28(6), 6695-6726. https://doi.org/10.1007/s10639-022-11431-8
Wayman, J. C., & Stringfield, S. (2006). Technology-supported involvement of entire faculties in examination of student data for instructional improvement. American Journal of Education, 112(4), 549–571. https://doi.org/10.1086/505059
Widiastuti, I. (2025). Assessing the Impact of Education Policies in Indonesia: Challenges, Achievement, and Future Direction. Al-Ishlah: Jurnal Pendidikan, 17(2), 1955-1964. https://doi.org/10.35445/alishlah.v17i2.6803
Williams, N. J., Frederick, L., Ching, A., Mandell, D., Kang-Yi, C., & Locke, J. (2021). Embedding school cultures and climates that promote evidence-based practice implementation for youth with autism: A qualitative study. Autism, 25(4), 982-994. https://doi.org/10.1177/1362361320974509
Zhao, Y., Liu, Y., & Wang, R. (2023). A study on improving continuing education in scientific organizations through information technology. International Journal for Infonomics, 16(1), 2152-2163.
Copyright (c) 2026 Journal La Edusci

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



