From Normative Awareness to Workflow Confidence: Self-Reported Data Science Literacy in China's Local Public Sector

Digital-government reform depends on public-sector personnel who can move evidence from problem framing to accountable action, yet empirical research often measures statistical skill or broad digital literacy rather than the full workflow. This study examines self-reported confidence across data generation, collection, cleaning, management, analysis, communication, application, and ethics. A cross-sectional online survey recruited personnel through professional networks in Hunan, Guangdong, and Sichuan, China. Of 512 returns, two fell below the prespecified 60-second completion threshold, leaving 510 cases. The analysis reports internal consistency, psychometric diagnostics, descriptive profiles, correlations, and exploratory Welch group comparisons, together with duration- and scoring-rule sensitivity checks. The item-weighted overall mean was near the five-point scale midpoint (M = 3.048, SD = .771). Ethics ranked first (M = 3.565), followed by data generation (M = 3.541); collection ranked last (M = 2.813), with communication (M = 2.867) and analysis (M = 2.929) also below the midpoint. Respondents expressed least confidence in programming, web acquisition, application programming interfaces, metadata, advanced methods, and interactive visualization. The gender mean difference was negligible (0.040 points, 95% CI [-0.104, 0.184], Hedges g = .052). Welch tests showed omnibus variation across age, education, organizational level, and position, but small groups, unusually low within-group variance, and empty cross-classifications preclude independent or causal interpretations. The study contributes a workflow-completeness lens and identifies a normative-operational confidence gap in this sample while keeping reported confidence distinct from demonstrated performance and organizational capability. Under these evidentiary limits, it supports authentic public-data projects, role-differentiated development, and ethical judgment embedded throughout technical work.

Public managers, data science literacy, digital government, public administration education

1. Bonikowska, A., Sanmartin, C., & Frenette, M. (2019). Data literacy: What it is and how to measure it in the public service. Statistics Canada, Analytical Studies: Methods and References, No. 22. https://publications.gc.ca/site/eng/9.877889/publication.html [Google Scholar] [Crossref]

2. Busuioc, M. (2021). Accountable artificial intelligence: Holding algorithms to account. Public Administration Review, 81(5), 825-836. https://doi.org/10.1111/puar.13293 [Google Scholar] [Crossref]

3. Central Committee of the Communist Party of China & State Council of the People's Republic of China. (2023). Overall layout plan for the construction of Digital China [In Chinese]. https://www.moe.gov.cn/jyb_xwfb/s6052/moe_838/202302/t20230228_1048220.html [Google Scholar] [Crossref]

4. Dingelstad, J., Borst, R. T., & Meijer, A. (2022). Hybrid data competencies for municipal civil servants: An empirical analysis of the required competencies for data-driven decision-making. Public Personnel Management, 51(4), 458-490. https://doi.org/10.1177/00910260221111744 [Google Scholar] [Crossref]

5. Eaves, D., Mergel, I., Clarke, A., Fetter, J., & Steinberg, T. (2026). Teaching public service in the digital age: Collectively building a global curriculum to teach digital-era competencies to public managers. Information Polity, 31(1), 43-60. https://doi.org/10.1177/15701255251382598 [Google Scholar] [Crossref]

6. Ho, K. K. W., Li, N., & Sayama, K. C. (2024). Equip public managers with data analytics skills: A proposal for the new generation of MPA/MPP programs with data science track. Library Hi Tech, 42(4), 1286-1304. https://doi.org/10.1108/LHT-07-2022-0320 [Google Scholar] [Crossref]

7. Hsu, Y.-C. (2024). Mapping the landscape of data science education in higher general education in Taiwan: A comprehensive syllabi analysis. Education Sciences, 14(7), Article 763. https://doi.org/10.3390/educsci14070763 [Google Scholar] [Crossref]

8. Kleinschmit, S. (2019). An inclusive framework for data science competencies in NASPAA MPA and MPP programs [White paper]. Network of Schools of Public Policy, Affairs, and Administration. https://www.naspaa.org/sites/default/files/docs/2019-09/NASPAA%20Whitepaper%20-%20Kleinschmit.pdf [Google Scholar] [Crossref]

9. Lavertu, S. (2016). We all need help: "Big data" and the mismeasure of public administration. Public Administration Review, 76(6), 864-872. https://doi.org/10.1111/puar.12436 [Google Scholar] [Crossref]

10. Madan, R., & Ashok, M. (2023). AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Government Information Quarterly, 40(1), Article 101774. https://doi.org/10.1016/j.giq.2022.101774 [Google Scholar] [Crossref]

11. Mergel, I., Edelmann, N., & Haug, N. (2019). Defining digital transformation: Results from expert interviews. Government Information Quarterly, 36(4), Article 101385. https://doi.org/10.1016/j.giq.2019.06.002 [Google Scholar] [Crossref]

12. Mergel, I., Rethemeyer, R. K., & Isett, K. (2016). Big data in public affairs. Public Administration Review, 76(6), 928-937. https://doi.org/10.1111/puar.12625 [Google Scholar] [Crossref]

13. Organisation for Economic Co-operation and Development. (2021). The OECD framework for digital talent and skills in the public sector (OECD Working Papers on Public Governance No. 45). OECD Publishing. https://doi.org/10.1787/4e7c3f58-en [Google Scholar] [Crossref]

14. Ongena, G. (2023). Data literacy for improving governmental performance: A competence-based approach and multidimensional operationalization. Digital Business, 3(1), Article 100050. https://doi.org/10.1016/j.digbus.2022.100050 [Google Scholar] [Crossref]

15. Overton, M., & Kleinschmit, S. (2022). Data science literacy: Toward a philosophy of accessible and adaptable data science skill development in public administration programs. Teaching Public Administration, 40(3), 354-365. https://doi.org/10.1177/01447394211004990 [Google Scholar] [Crossref]

16. Overton, M., & Kleinschmit, S. (2023). Transforming research methods education through data science literacy. Teaching Public Administration, 41(2), 149-169. https://doi.org/10.1177/01447394221084488 [Google Scholar] [Crossref]

17. Ruijer, E., & Lember, V. (2026). Public sector capabilities and individual skills needed for the digital transition: A multilevel perspective on teaching digital governance. Information Polity, 31(1), 4-12. https://doi.org/10.1177/15701255261423332 [Google Scholar] [Crossref]

18. State Council of the People's Republic of China. (2022). Guiding opinions on strengthening the construction of digital government [In Chinese] (State Council Document No. 14). https://app.www.gov.cn/govdata/gov/202206/23/486412/article.html [Google Scholar] [Crossref]

19. United Nations Department of Economic and Social Affairs. (2024). UN e-government survey 2024: Accelerating digital transformation for sustainable development. United Nations. https://desapublications.un.org/publications/un-e-government-survey-2024 [Google Scholar] [Crossref]

20. Zhang, H., & Yang, T. (2023). The concept, structure and measurement of civil servants' data literacy [In Chinese]. Digital Library Forum, (1), 58-65. https://doi.org/10.3772/j.issn.1673-2286.2023.01.008 [Google Scholar] [Crossref]

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