Analyzing the Effect of Intelligent Technologies on the Efficiency of the Government Budgeting System with Emphasis on Performance-Based and Data-Driven Budgeting
Keywords:
Intelligent technologies, government budgeting, performance-based budgeting, data-driven budgeting, artificial intelligence, budget efficiency, public financial managementAbstract
This study aimed to analyze the effect of intelligent technologies on improving the efficiency of the government budgeting system, with particular emphasis on performance-based budgeting, data-driven decision-making, fiscal transparency, and the quality of public resource allocation. This study employed a qualitative research design using thematic analysis. The research population consisted of budgeting experts, public financial managers, planning and budget specialists, government financial information system experts, and university faculty members in public administration and public finance in Tehran. Participants were selected through purposive and snowball sampling, and data collection continued until theoretical saturation was achieved. A total of 22 semi-structured interviews were conducted. After full transcription, the data were analyzed using NVivo software through open, axial, and selective coding. To enhance trustworthiness, member checking, peer review, audit trail documentation, and constant comparison were applied throughout the analysis process. The findings indicated that intelligent technologies influence the efficiency of the government budgeting system through five major categories: integration of financial and performance data, intelligent resource allocation, reinforcement of performance-based budgeting, enhancement of budget transparency and accountability, and redesign of institutional capacities and data governance. Participants emphasized that artificial intelligence, big data analytics, managerial dashboards, integrated financial management systems, and predictive algorithms can reduce human error, discretionary decision-making, information delays, and inefficient allocation of public resources. Nevertheless, poor data quality, organizational resistance, fragmented systems, weak performance indicators, and ethical concerns regarding algorithmic decision-making were identified as key barriers. The results suggest that intelligent technologies can improve the efficiency of government budgeting only when they are embedded within a coherent framework of data governance, institutional transparency, valid performance indicators, system interoperability, and specialized human capacity. Accordingly, intelligent budgeting should not be viewed merely as a technological project, but as an institutional, managerial, and data-driven transformation in public financial management.
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