Big data and the measurement of public organizations' performance and efficiency: The state-of-the-art
Open accessAbstract
The increasing availability of statistical data raises opportunities for ‘big’ data and learning analytics. Here, we review the academic literature and research relating to the use of big data analytics in the public sector, and its contribution to public organizations’ performance and efficiency. We outline the advantages as well as the limitations of using big data in public sector organizations and identify research gaps in recent studies and interesting areas for future research.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The public value of duty is characterised by using AI in facilitating the democratic will by enabling citizen engagement and participation at scale.
“The public value of duty is characterised by using AI in facilitating the democratic will by enabling citizen engagement and participation at scale”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Rogge et al., 2017
The “Big data and public sector: Opportunities” section says big-data technologies can help governments foster civic participation, citizen engagement, and online democracy; it also describes widespread adoption of interactive tools by cities. It does not characterize this as a public value of duty or establish that AI, specifically, enables participation at scale. The indexed passages provide no page numbers. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 78 · Cited by 98
Research works printed but not linked: 3
- [10]10. Brown B, Chui M and Manyika J (2011) Are you ready for the era of 'big data'. McKinsey Quarterly 4: 24-35.
- [66]66. O'Reilly T (2010) Government as platform. In: Lathrop D and Ruma L (eds) Open Government: Collaboration, Transparency and Participation in Practice. Sebastopol, CA: O'Reilly Media Inc., pp.11-39.
- [77]77. Tene O and Polonetsky J (2012) Privacy in the age of big data -A time for big decisions. Stanford Law Review (online) 64, 63.
- Authors
- Rogge, N., Agasisti, T., & De Witte, K.
- Affiliations
- KU Leuven (BE)
- Politecnico di Milano (IT)
- Leuven Economics of Education Research, KU Leuven Top Institute for Evidence Based Education Research, University of Maastricht, Netherlands
- Corresponding author
- Agasisti, Tommaso
- Venue
- Public Policy and Administration
- Publisher
- SAGE Publications
- Published
- 2017-01-17
- Volume
- 32
- Issue
- 4
- Pages
- 263-281
- ISSN
- 0952-0767 (ISSN-L)
- 1749-4192
- OpenAlex
- W2574816137
- Cited by
- 98
- References
- 78
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 14 · 2023: 14 · 2022: 20 · 2021: 13 · 2020: 13 · 2019: 8 · 2018: 4 · 2017: 3
- FWCI
- 37.23
- Type
- journal article
- Language
- en
- Open access
- Locations
- Public Policy and Administration (publishedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Lirias (submittedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- Topics
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Social Sciences › Public Administration › Public Policy and Administration Research
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- big data analytics, public sector performance, organizational efficiency, public sector organizations, learning analytics, performance measurement, research gaps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
