Utilizing data platform management to implement "5W" analysis framework for preventing and controlling corruption in...

Area of Science:

• Public Administration and Governance
• Information Technology and Big Data Analytics
• Corruption Studies

Background:

• Information asymmetry remains a key challenge in grass-roots government corruption.
• Existing anti-corruption efforts often overlook the potential of data platforms and analytical frameworks.
• There is a need for innovative approaches to enhance the effectiveness of corruption prevention and control.

Purpose of the Study:

• To introduce and evaluate data platform management integrated with the 5W (Who, What, When, Where, Why) analysis framework for grass-roots corruption control.
• To assess the synergistic impact of SWOT, PDCA, and 5W analysis frameworks on corruption prevention.
• To enhance the efficacy of grass-roots government governance through data-driven strategies.

Main Methods:

• Implementation of data platform management combined with SWOT, PDCA, and 5W analysis frameworks.
• Iterative evaluation of the integrated models over various timeframes.
• Assessment of impact using indicators such as public participation and government satisfaction.

Main Results:

• The 5W model increased public participation scores by 10.76% and governance efficiency by 23.24%.
• SWOT analysis proved effective in formulating targeted strategies and improving corruption reporting rates.
• The synergistic application of SWOT, PDCA, and 5W models demonstrated a significant positive impact on corruption prevention.

Conclusions:

• Data platform management, when combined with multi-model analysis (SWOT, PDCA, 5W), effectively enhances grass-roots government corruption prevention.
• This integrated approach offers a scalable solution for improving transparency and efficiency in local governance.
• The study provides valuable insights for developing data-driven strategies in public administration.