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In the era of information technology advancement, big data analysis has emerged as a crucial tool for government governance. Despite this, corruption remains a challenge at the grass-roots level, primarily attributed to information asymmetry. To enhance the efficacy of corruption prevention and control in grass-roots government, this study introduces the concept of data platform management and integrates it with the "5W" (Who, What, When, Where, Why) analysis framework. The research is motivated by the observation that existing studies on corruption prevention primarily concentrate on the formulation of laws and regulations, neglecting the potential improvement in actual effectiveness through the utilization of data platforms and analytical frameworks. The research employs methodologies grounded in the Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis framework, the Plan, Do, Check, Act (PDCA) cycle analysis framework, and the 5W analysis framework. Throughout the iterative process of implementing data platform management, various timeframes are established, and the impact of the three models is evaluated using indicators such as public participation and government satisfaction. The research reveals that the SWOT framework can formulate targeted strategies, the PDCA framework continuously optimizes work processes, and the 5W framework profoundly explores the root causes of corruption. The outcomes indicate a 10.76% increase in the public participation level score with the 5W model, rising from 71.67%, and a 23.24% increase in the governance efficiency score, reaching 66.12%. The SWOT model excels in case handling prescription and corruption reporting rate. The synergistic application of the three models demonstrates a positive impact. In conclusion, the amalgamation of data platform management and a multi-model approach effectively enhances the corruption prevention capabilities of grass-roots governments, offering insights for the establishment of transparent and efficient grass-roots governance.
Wang et al. (Fri,) studied this question.