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

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.

Keywords:

• Big data
• literature review
• mapping knowledge domains
• public services

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 See Zhong et al., Evaluation of Chinese Cities' Basic Public Service Capability (2019).

2 See Hao et al., “Emergency Management Information”.

3 See Huang and Yu, “Leading Digital Technologies for Coproduction”.

4 See Xinhuanet, “The Year of the ‘Internet Plus’ Action Plan”.

5 See Xinhuanet, “Implementing the National Big Data Strategy”.

6 See Xinhuanet, “China’s New Generation of Artificial Intelligence (AI)”.

7 See Agrawal et al., “Challenges and Opportunities with Big Data”.

8 See Schmitter, “The Elusive Concept of ‘Governance’”; Gang, “The Politics of Local Justice Expenditure in China”.

9 See Meng and Li, “Political Interaction in Cyberspace”; Su and Meng, “Selective Responsiveness”; Tang et al., “Do Authoritarian Governments Respond to Public Opinion on the Environment?”.

10 See Jonathan, “China's Responsiveness to Internet Opinion”; Distelhorst and Hou, “Ingroup Bias in Official Behavior”; Chen et al., “Sources of Authoritarian Responsiveness”.

11 See Xia and Li, “A Fresh Idea in Public Services”.

12 See Frankel and Reid, “Big Data: Distilling Meaning from Data”.

13 See Manyika et al., “Big Data: The Next Frontier”.

14 See Agrawal et al., “Challenges and Opportunities with Big Data”.

15 See Floridi, “Big Data and Their Epistemological Challenge”.

16 See Agrawal et al., “Challenges and Opportunities with Big Data”.

17 See Liu and Zhang, “Research Overview of Big Data Technology”.

18 See CNKI, “CNKI Help - Product Usage - How to use the Database”.

19 See Chen, “Science Mapping”.

20 Based on acquired literature. The data is captured using Excel’s search function.

21 See Gu and Wang, “The Analysis about Literature Metrology”.

22 See Chen, Mapping Scientific Frontiers.

23 See Ning, “The ‘Internet Plus’ Action Plan”.

24 See Liu and Peng, “‘Internet Plus’ Government”.

25 See He and Li, “‘Internet Plus’ Public Services”.

26 See Zhang and Li, “Big Data-Driven Smart Public Services”.

27 See Fei et al., “The Idea and Path of ‘Internet Plus Government Services’”.

28 See Liu and Tan, “Data-Driven Smart Services”.

29 See Zhang, “The ‘Internet Plus Government Services’ Model Innovation”.

30 See Si and Hu, “The Concept, Logic, Path, and Role of Value Co-creation”.

31 See Fan and Zhao, “Research on the Problems and Countermeasures”; Xia et al., “Service-Oriented Government in China”.

32 See Wang, “Using the Internet of Things”.

33 See Sun and Yuan, “Government Public Service Platform”.

34 See Li and Zheng, “the Innovation of Government Services”.

35 See Li, “Research on the ‘Internet+’-Based Reform”.

36 See Tao, “Innovation of Government Public Services”.

37 See Yu et al., “Research on the Block-Chain-Based Sharing Model”.

38 See Chen et al., “Research on the Application of Artificial Intelligence”.

39 See Xu and Zhu, “Smart Government”; Wang, “Openness and Integration”.

40 See Li and Chen, “Cloud Government Service”.

41 See Zhang, “Duplex Transition”.

42 See Hu et al., “Big Data-Based Governance Innovation”.

43 See Chen Tan and Chen Yun, “The Future of Government”.

44 See Meng, “Intelligent Social Governance”.

45 See Zuo and Wang, “Government Cross-Sectoral Data Governance Framework”.

46 See Rao, “State Governance in the Big Data”.

47 See Wang, “The Transformation of Social Governance”.

48 See Lu et al., “Reflections on the Top-level Design of Smart Cities”.

49 See Liu and Li, “Smart City Governance”.

50 See Jiang and Zhang, “the Key Questions of Wisdom Community”.

51 See Chen and Liu, “Urban Community Governance Informatization”.

52 See Xu and Zhang, “The Precision of Public Services”.

53 See Ding, “the Supply Models of Basic Public Services”.

54 See Liu, “Public Service Supply Driven by Big Data”.

55 See Zhang et al., “Big Data-Driven Public Services Delivery.”; Li, “Mode Innovation of Public Service Supply”.

56 See Xing, “The Targeted Supply of Internet Plus Local Government Services”.

57 See Rong, “Accurate Recognition Mechanism of Public Service Demand”; Ning et al., “Big Data-Driven Supply and Demand Matching”.

58 See Deng and Li, “Towards Precision”; Dang and Du, “Demand Identification and Targeted Supply”.

59 See Guo and Lin, “Algorithm Injustice and Big Data Ethics”; Gu et al., “Global Justice Index Report 2020”; Gu et al., “Global Justice Index Report”.

60 When generating the knowledge map, CiteSpace also generates a table containing the keyword frequency, centrality, and year of first appearance, which are cited here. If interested, contact the author for the table.

61 See Xia and Li, “A Fresh Idea in Public Services”.

62 See Zhang, “Artificial Intelligence and Public Services”.

63 See Xue, “Construction of and Prospects for Public Administration Subjects in China”.

64 See Xia and Li, “A Fresh Idea in Public Services”.

Additional information

Funding

This work was supported by the National Social Science Fund of China (No. 20&ZD112).

Notes on contributors

Zhiqiang Xia

Zhiqiang Xia, Ph.D. in Economics, is a professor (Ph.D. Supervisor) at the School of Public Administration at Sichuan University, Chengdu, China. Dr. Xia is a member of the Chinese Political Science Teaching Steering Committee. He is also the chief expert in charge of major scientific research projects supported by the National Social Science Fund of China. He conducts field research on fundamental theories of political science and public administration, public services, and public policy.

Xingyu Yan

Xingyu Yan is a doctoral student at the School of Public Administration at Sichuan University, Chengdu, Sichuan Province, China. His research interests include political ethics and public service.

Xiaoyong Yang

Xiaoyong Yang is a doctoral student at the School of Public Administration at Sichuan University, Chengdu, Sichuan Province, China. His research focus is on public services and social governance.