Distributed Knowledge Systems for Resource Allocation and Management in Higher Education A decision making model

In this digital transformation era, distributed knowledge-based systems have emerged as a possible prescription in the higher education resource allocation paradigm. Scholars have noted that data-driven decision-making is transforming the management of academic institutions, optimization of resource distribution, and the strategic planning, faculty development, and technological integration of universities across the global educational landscape. The paper attempts to move forward research in resource allocation models from traditional, centralized, and fixed-budget contexts to emerging dynamic frameworks that address current institutional complexities. In proposing such a systematic decision-making model, the authors reason why Analytical Hierarchy Process (AHP) studies may be particularly suited for the iterative prioritization, evaluation, and refinement of findings in the form of decision-support tools such as SuperDecisions software. Additionally, the AHP-based framework is used to organize a hierarchical assessment of resource allocation strategies to identify some best practices related to specific educational investment areas. This structured model then furthers the examination of the theoretical and practical implications related to the use of distributed knowledge systems in terms of academic infrastructure, faculty advancement, and student support mechanisms. A closing case finally examines the role of a prominent higher education institution (i.e., Tashkent State University of Economics) in the resource management-decision-making nexus. Their effect is to increase the efficiency and sustainability of universities operating using the AHP-driven allocation model based on the aforementioned empirical findings.

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