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Big Data and AI – A transformational shift for government: So, what next for research?

Pencheva, I; Esteve, M; Mikhaylov, SJ; (2018) Big Data and AI – A transformational shift for government: So, what next for research? Public Policy and Administration 10.1177/0952076718780537. (In press). Green open access

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Abstract

Big Data and artificial intelligence will have a profound transformational impact on governments around the world. Thus, it is important for scholars to provide a useful analysis on the topic to public managers and policymakers. This study offers an in-depth review of the Policy and Administration literature on the role of Big Data and advanced analytics in the public sector. It provides an overview of the key themes in the research field, namely the application and benefits of Big Data throughout the policy process, and challenges to its adoption and the resulting implications for the public sector. It is argued that research on the subject is still nascent and more should be done to ensure that the theory adds real value to practitioners. A critical assessment of the strengths and limitations of the existing literature is developed, and a future research agenda to address these gaps and enrich our understanding of the topic is proposed.

Type: Article
Title: Big Data and AI – A transformational shift for government: So, what next for research?
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/0952076718780537
Publisher version: http://dx.doi.org/10.1177/0952076718780537
Language: English
Additional information: © 2018, The Author(s) 2018. This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Big Data, literature review, policy process
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Political Science
URI: https://discovery.ucl.ac.uk/id/eprint/10055916
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