Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies
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The introduction of traditional digital government initiatives has already been documented as helping reduce public servant corruption.
“The introduction of traditional digital government initiatives has already been documented as helping reduce public servant corruption (Bertot, Jaeger, & Grimes, 2010):”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Bertot, Jaeger, & Grimes, 2010
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Access Denied The Practice and Policy of Global Internet Filtering
Deibert, R., Palfrey, J., Rohozinski, R., & Zittrain, J. L. (2008) · CrimRxiv · doi:10.21428/cb6ab371.9034e9f7 · Cited by 358 · see citing works
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Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · see citing works
Benefits, adoption barriers and myths of open data and open government
Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012) · Information Systems Management · doi:10.1080/10580530.2012.716740 · Cited by 2,062 · see citing works
Social Media, Echo Chambers, and Political Polarization
Barberá, P. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.004 · Cited by 373 · see citing works
Why e-government projects fail? An analysis of the Healthcare.gov website
Anthopoulos, L., Reddick, C. G., Giannakidou, I., & Mavridis, N. (2016) · Government Information Quarterly · doi:10.1016/j.giq.2015.07.003 · Cited by 325 · see citing works
Big data and AI - A transformational shift for government: So, what next for research?
Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2020) · Public Policy and Administration · doi:10.1177/0952076718780537 · Cited by 272 · see citing works
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Transparency and Trust in Government. Evidence from a Survey Experiment
Alessandro, M., Cardinale Lagomarsino, B., Scartascini, C., Streb, J., & Torrealday, J. (2021) · World Development · doi:10.1016/j.worlddev.2020.105223 · Cited by 135 · see citing works
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Ingrams, A., Kaufmann, W., & Jacobs, D. (2022) · Policy & Internet · doi:10.1002/poi3.276 · Cited by 133 · see citing works
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Latent transparency and trust in government: Unexpected findings from two survey experiments
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An Empirical Evaluation Of The Potential Of Public E-Procurement To Reduce Corruption
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Does E-Government Raise Effectiveness and Efficiency?: Examining the Cross-National Effect
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Examining the Determinants of Police Department Transparency: The View of Police Executives
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- Authors
- Bertot, J. C., Jaeger, P. T., & Grimes, J. M.
- Affiliations
- University of Maryland, College Park (US)
- Corresponding author
- Grimes, Justin M.
- Venue
- Government Information Quarterly
- Publisher
- Elsevier BV
- Published
- 2010-07
- Volume
- 27
- Issue
- 3
- Pages
- 264-271
- ISSN
- 0740-624X (ISSN-L)
- OpenAlex
- W2096870307
- Cited by
- 2,451
- References
- 1
- Citations by year
- 2026: 222 · 2025: 167 · 2024: 124 · 2023: 148 · 2022: 134 · 2021: 162 · 2020: 194 · 2019: 190 · 2018: 201 · 2017: 198 · 2016: 173 · 2015: 166 · 2014: 159 · 2013: 100 · 2012: 75
- FWCI
- 421.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Government Information Quarterly (publishedVersion)
- Topics
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Social Sciences › Political Science and International Relations › Administrative Law and Governance
- Social Sciences › Communication › Social Media and Politics
- Keywords
- e-government, social media, transparency, anti-corruption, information and communication technologies, social change, open government
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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.
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