Integration of Distributed Knowledge-Based Systems for Improving Financial Autonomy and Management Decision-Making in Higher Education: A Task Mapping Perspective

The decision-making process, especially among various academic departments, is expected to be heavily influenced by knowledge-based systems since it uses automated mapping and large amounts of institutional data. The objective of this study was to test the effectiveness of task-based mapping in improving the financial autonomy of higher education institutions (HEIs), a vulnerable and underfunded public species in emerging economies, from manual budgeting practices by a distributed intelligent system based on an ontology-driven engine. The model then uses the operational definition of autonomy and SEM–PSM model to analyze how task allocation supports the efficiency of budget planning and uses the semantic and rule-based bases to predict administrative outcomes. A task-mapping prototype is developed, with clusters of financial records, decision nodes, governance attributes, and performance indicators considering contextual constraints, knowledge flows, and institutional hierarchies. Survey responses and a structural equation modeling model (SEM) were generated as observational data from the pilot implementation sites. The result shows that the proposed distributed architecture with a focus on the knowledge nodes collected by the institutional monitoring system provided a promising method for improving the transparent distribution of the budget, which serves as a replacement for centralized control in the attempt to execute a wide-scale autonomy initiative by the Ministry of Higher Education. Overall, results show that task mapping is linked with an 18% reduction in decision latency in universities with digital infrastructure. The analysis of the decision optimization module indicates that the systemic influence of the distributed agents in financial planning has a statistically significant effect on the financial resilience of the institutions and the confidence level of the administrative teams.

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