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Cited by 162E-Government and Public ServicesInformation Systems Theories and ImplementationPublic Policy and Administration Research

The DOI matches its Crossref record, with a small difference in title or year; 50 of the 50 references it cites were found in a registry or an index.

Overcoming the Challenges of Collaboratively Adopting Artificial Intelligence in the Public Sector

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Campion, Averill, Gasco-Hernandez, Mila, Jankin Mikhaylov, Slava, Esteve, Marc·2022·Social Science Computer Review
DOI 10.1177/0894439320979953
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Abstract

Despite the current popularity of artificial intelligence (AI) and a steady increase in publications over time, few studies have investigated AI in public contexts. As a result, assumptions about the drivers, challenges, and impacts of AI in government are far from conclusive. By using a case study that involves a large research university in England and two different county councils in a multiyear collaborative project around AI, we study the challenges that interorganizational collaborations face in adopting AI tools and implementing organizational routines to address them. Our findings reveal the most important challenges facing such collaborations: a resistance to sharing data due to privacy and security concerns, insufficient understanding of the required and available data, a lack of alignment between project interests and expectations around data sharing, and a lack of engagement across organizational hierarchy. Organizational routines capable of overcoming such challenges include working on-site, presenting the benefits of data sharing, reframing problems, designating joint appointments and boundary spanners, and connecting participants in the collaboration at all levels around project design and purpose.

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Research works printed but not linked: 6

  1. Bernard H. R., 2017, Research methods in anthropology: Qualitative and quantitative approaches
  2. Christodoulou P., 2018, Da makes the public sector go round [Conference session]
  3. European Commission, 2019, A definition of AI: Main capabilities and scientific disciplines
  4. Gasco M., 2011, Interoperability in the justice field: Variables that affect implementation [Conference session]
  5. Korinek A., 2017, Artificial intelligence and its implications for income distribution and inequality
  6. Mehr H., 2017, Artificial intelligence for citizen services and government

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Metadata

Authors
Campion, A., Gasco-Hernandez, M., Jankin Mikhaylov, S., & Esteve, M.
Affiliations
  • Universitat Ramon Llull (ES)
  • University at Albany, State University of New York, Albany, NY, USA
  • Hertie School, Berlin, Germany
  • Ramon Llull University, Barcelona, Spain
Venue
Social Science Computer Review
Publisher
SAGE Publications
Published
2020-12-20
Volume
40
Issue
2
Pages
462-477
ISSN
  • 0894-4393 (ISSN-L)
  • 1552-8286
DOI
10.1177/0894439320979953
OpenAlex
W3118099700
Cited by
162
References
50
Citations by year
2026: 41 · 2025: 52 · 2024: 34 · 2023: 22 · 2022: 9 · 2021: 4
FWCI
30.21
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Social Science Computer Review (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
Topics
  • Social Sciences › Political Science and International Relations › E-Government and Public Services
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
  • Social Sciences › Public Administration › Public Policy and Administration Research
Keywords
artificial intelligence adoption, public sector, inter-organizational collaboration, data sharing, privacy and security concerns, organizational routines, boundary spanners, county councils, university-government collaboration, organizational hierarchy

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