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
Open accessAbstract
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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“Some changes result from external factors (4a) that include the societal context (Rahwan 2018), others usually include aspects of inter-organizational collaboration (Campion et al. 2020) with an ecology of external partners.”
From Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence (2023) · cites this paper as Campion et al. 2020
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Cites
(1984) · Cited by 81,983
Designing and Conducting Mixed Methods Research
Robinson, P. M. (2007) · Australian and New Zealand Journal of Public Health · doi:10.1111/j.1753-6405.2007.00096.x · Cited by 23,362
Case study research: design and methods
Phelan, S. (2011) · Evaluation & Research in Education · doi:10.1080/09500790.2011.582317 · Cited by 21,904
(2014) · Cited by 15,747
Designing qualitative research
Shaliha, F., Mozaffari, M., Ramezani, F., Hajnasiri, H., & Moafi, F. (1989) · Choice Reviews Online · doi:10.5860/choice.27-1232 · Cited by 9,914
Collaborative Governance in Theory and Practice
Ansell, C., & Gash, A. (2008) · Journal of Public Administration Research and Theory · doi:10.1093/jopart/mum032 · Cited by 8,256
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 · cited 10×
“It also shows the importance of trust among stakeholders (Dwivedi et al., 2019; Kankanhalli et al., 2019; Mikhaylov et al., 2018).”
BackgroundInfluentialReconceptualizing Organizational Routines as a Source of Flexibility and Change
Feldman, M. S., & Pentland, B. T. (2003) · Administrative Science Quarterly · doi:10.2307/3556620 · Cited by 4,020
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.01.021 · Cited by 2,628
Two Decades of Developments in Qualitative Inquiry: A Personal, Experiential Perspective
Patton, M. Q. (2002) · Qualitative Social Work · doi:10.1177/1473325002001003636 · Cited by 2,564
Research works printed but not linked: 6
- Bernard H. R., 2017, Research methods in anthropology: Qualitative and quantitative approaches
- Christodoulou P., 2018, Da makes the public sector go round [Conference session]
- European Commission, 2019, A definition of AI: Main capabilities and scientific disciplines
- Gasco M., 2011, Interoperability in the justice field: Variables that affect implementation [Conference session]
- Korinek A., 2017, Artificial intelligence and its implications for income distribution and inequality
- Mehr H., 2017, Artificial intelligence for citizen services and government
Cited by
Sorted by citations
Madan, R., & Ashok, M. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2022.101774 · Cited by 468
“…et al., 2018; Wirtz & Müller, 2018) IT capabilities • Current capabilities in managing IT assets (Aboelmaged & Mouakket, 2020; Ballester, 2021; Campion et al., 2020; Casalino et al., 2020; Erkut, 2020; Janssen, Brous, et al., 2020; Janssen, Hartog, Matheus, Yi Ding, & Kuk, 2022; Kuziemski &…”
InfluentialExploring artificial intelligence adoption in public organizations: a comparative case study
Neumann, O., Guirguis, K., & Steiner, R. (2024) · Public Management Review · doi:10.1080/14719037.2022.2048685 · Cited by 451
“Campion et al. (2020) focused on inter-organizational collaborations in AI adoption.”
BackgroundThe implementation of artificial intelligence in organizations: A systematic literature review
Lee, M. C. M., Scheepers, H., Lui, A. K. H., & Ngai, E. W. T. (2023) · Information & Management · doi:10.1016/j.im.2023.103816 · Cited by 280
Herrmann, T., & Pfeiffer, S. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01391-5 · Cited by 173
Implementing AI in the public sector
Mergel, I., Dickinson, H., Stenvall, J., & Gascó, M. (2023) · Public Management Review · doi:10.1080/14719037.2023.2231950 · Cited by 184
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
- 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
- green
- discovery.ucl.ac.uk
- 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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