| 英文摘要 |
In the context of generative AI and data-driven governance, data governance in the public sector has become an institutional capacity, rather than just a technical, issue. This study integrates data lifecycle theory and maturity models to develop the Data Lifecycle Management-based Data Governance Maturity Model (DLM-DGMM). Based on a questionnaire survey of Taiwan’s Ministry of the Interior system, the study assesses the current state of data governance maturity and diagnoses structural challenges from a civil service perspective. The findings indicate that overall maturity is steady at Level 3, reflecting the establishment of standardized and sustainable processes. However, a clear imbalance exists across lifecycle stages: data archiving scores highest, while data analytics remains underdeveloped, suggesting a“preservation-over-application”pattern shaped by administrative standardization. Group comparisons reveal perceptual differences across generations, hierarchical ranks, and professional roles. Yet, after controlling for bureaucratic attributes, information literacy shows a significant positive association with perceived maturity, whereas traditional status-based variables have limited explanatory power. These results point to a paradigm shift from status-based governance to capability-based governance. The study therefore proposes institutionalizing the DLM-DGMM within recruitment, promotion, and training systems to support diagnostic, evidence-based civil service reform and strengthen institutional capacity for digital governance. |