Development of an AI-DSS model to support financial and managerial decision-making in higher education institutions through the integration of Smart Campus systems
Abstract
By measuring the performance of decision-making processes carried out by higher education institutions in the Smart Campus environment and identifying the main determinant factors influencing this process (including criteria from the different stakeholder groups), this study contributes to deepening our understanding of the dynamics of financial and managerial decisions in universities operating in emerging economies and, particularly, in the Uzbekistan context, for which no previous empirical research on this topic could be found. The objective of this paper is to analyze the potential of AI-DSS models to improve the quality of decisions formed in a Smart Campus system. Model development focused on application of parametric survival models, AHP, and hybrid model integration, and included financial and managerial indicators and a comparative analysis. Data was collected using in vivo campus transactions, administrative records, survey recordings along with financial reports and operational data in public, private, or mixed universities. The results reveal that the level of decision support utilization is low in the sample institutions and that the size of the organization, the inputs from stakeholders, and the experience and cognitive orientation of the institution's top managers have a significant influence on the level of adoption. The results show that when AI-DSS adoption is higher, the accuracy and robustness are higher and the uncertainty is smaller with a high predictive reliability compared with a low adoption level. In terms of policy implications, findings suggest that universities should play a more active role in institutionalizing AI-DSS in higher education, particularly in the area of financial and managerial decision-making.