Research on big data-driven public services in China: a visualized bibliometric analysis

Zhiqiang Xia, Xingyu Yan and Xiaoyong Yang

Journal of Chinese Governance, 2022, vol. 7, issue 4, 531-558

Abstract: The gradual establishment of systematic, equalized, and standardized basic public services has drawn attention of the academic community to the mismatch between supply-demand, and public dissatisfaction. Big-data-driven public services innovatively attempt to solve these problems, and reflect the theoretical essence of the process by which big data can empower the responsiveness of governments. In this study, we adopted the theoretical frameworks of ‘diversified needs–selective responses’, ‘risk shocks–forward-looking responses’, and ‘forward-looking predictions–creative responses’. We propose that big data-driven public services should respond not only to present needs but also to social risks and future needs. Therefore, it is imperative to review the status, problems, and future directions of big data-driven public service research in China. This study uses bibliometric visualization analysis on data from research projects, monographs, and journal publications. The results reveal that the main research topics are basic theoretical issues, service-oriented government development guided by big data strategies, practical innovation of public services in the context of smart governance, and the effective supply of big data-driven public services. Previous studies suffered from weak theoretical reflection and construction, lacked relevant institutions, had less fine-grained and fragmented technical support, and lacked foresight and guidance. Attention should be paid to normative theories and institutions in big data-driven public services to ensure that these services are more targeted and prospective; creative research should be conducted. The systematic summarization of the current state of research and reflections on prospective and creative research trends will provide new ideas regarding future research directions.

Date: 2022
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DOI: 10.1080/23812346.2021.1947643

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