PDF(1284 KB)
Research on Data Openness Level Configuration for Provincial Government Data Open Platform
Xinyuan Lu, Zeyin Chen, Quan Lu, Xuelin Wang
Knowledge Management Forum ›› 2023, Vol. 8 ›› Issue (5) : 382-398.
PDF(1284 KB)
PDF(1284 KB)
Research on Data Openness Level Configuration for Provincial Government Data Open Platform
[Purpose/Significance] Starting with the perspective of configuration, this study aims to obtain the openness configuration of high-level government data and explore the factors which have deep effect on the openness level of government data. It also proposes different paths to optimize the level of data openness of the platform and summarize the optimization strategies. [Method/Process] With the qualitative comparative analysis method based on fuzzy set, taking the provincial government data open platform as research case, this study summarized five antecedent variables: data quantity, data quality, data specification, data acquisition and open range with the grounded theory method so as to explore the impact of the joint action of multiple variables on the openness level of government data. [Result/Conclusion] The research reveals three configurations. ①High standard data type: High standardized data on the platform, and good data quality. This type of platform can maintain a high level of data openness through two ways, one is to appropriately increase the number of data sets, and the other is to increase the number of API interfaces. ②Large quantity data type: large quantities of standardized data on the platform. The high level of data openness of this type of platform can be maintained only by increasing the openness of the volume of standardized data. ③High quality data type: good data quality on the platform. For platforms with good data quality, its data openness can reach a higher level by increasing the number of open data sets, increasing the scope of opening, and increasing the API interface for data acquisition.
government data openness / grounded theory / qualitative comparative analysis / fsQCA / NCA
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卢新元：提出研究主题与思路；
陈泽茵：撰写与修改论文；
卢 泉：检验数据；
王雪霖：检查与修改论文。
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