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Optimizing Government Document Processing for Administrative Efficiency
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PRE
AI
DOI：10.1080/08874417.2026.2688782.png)
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Abstract
En 中文
Government document processing systems face high costs, long processing times, and inconsistent performance under large transaction volumes. Unlike prior AI-based or centralized studies that emphasize adoption or system design, this study addresses post-adoption operational optimization in an existing blockchain-enabled system. Positioned within post-adoption IS performance literature, it examines how operational process redesign improves administrative outcomes without introducing new technologies. Using discrete-event simulation calibrated to 1.5 million annual transactions, the analysis compares baseline and optimized process designs within the same institutional setting. Findings show a 17% reduction in annual operating costs and a 26% reduction in average processing time (p < .001), with enhanced process stability. By quantifying how workflow redesign and coordination mechanisms affect cost, time, and stability, this study extends post-adoption IS performance literature beyond incremental contributions and provides actionable guidance for administrators and policymakers seeking to optimize document-intensive government services.
Keywords:
Administrative efficiency
government document processing
operational optimization
blockchain-enabled systems
decision support
simulation
Journal
IF:
4.2
Papers:
211
Citations:
3.1K
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