Research on the Driving Mechanism of Provincial Government Data Opening Service Quality from the Perspective of Information Ecology: Configuration Analysis Based on the Platform Data of 20 Provinces

doi:10.15936/j.cnki.1008-3758.2023.06.009

Abstract

Abstract: An in-depth analysis of the factors affecting the quality of government data opening services is conducive to improving the service level of government data opening and fully releasing the potential value of government data. From the perspective of information ecology， a theoretical analysis framework is constructed including elements of information， information people， information technology and information environment， and data information quality， data open attention， public data acquisition experience， data platform construction progress， and policies and regulations are extracted accordingly. Five condition variables are specified with the standard， and then the fuzzy set qualitative comparative analysis(fsQCA) is used to analyze the configuration of the 20 provincial government data open platform that has been launched. The results show that the driving mechanism of high-level service quality of provincial government data opening is subject-driven， object-driven and technology-driven. Each provincial government should adapt to local conditions， choose an effective path suitable for its own development， and continuously improve systematic thinking and strive to realize the combination and optimization of many elements， and at the same time strengthen efficient cooperation with enterprises， the public and other subjects， and further improve the quality of government data open services.

Key words: open government data; service quality; information ecology; configuration analysis

CLC Number:

D630

LI Ming， CAO Haijun. Research on the Driving Mechanism of Provincial Government Data Opening Service Quality from the Perspective of Information Ecology: Configuration Analysis Based on the Platform Data of 20 Provinces[J]. Journal of Northeastern University(Social Science), 2023, 25(6): 80-88.

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