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Cited by 69E-Government and Public ServicesPublic Policy and Administration ResearchPublic Administration and Political Analysis

A Realist Perspective on AI-era Public Management*

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Ojo, Adegboyega, Mellouli, Sehl, Ahmadi Zeleti, Fatemeh·2019·Proceedings of the 20th Annual International Conference on Digital Government Research
DOI 10.1145/3325112.3325261
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Abstract

Recent years have witnessed a number of significant ideas and approaches to addressing the shortcomings of the New Public Management paradigm. Three of these recent ideas, which include Digital Era Governance, Public Value Management, and New Public Governance, emphasise partnerships collaboration and engagement of citizens; performance governance and innovation and recognize the transformational potentials of digital technologies. Artificial Intelligence (AI) is one of the digital technologies attracting the greatest interest in public administration in terms of its potential impact. There are already a number of reports on how AI is being deployed in the public sector with good outcomes. By employing a realist review approach, this study investigates the specific mechanisms across post-NPM, organisational, individual and innovation contexts which are associated with positive outcomes from AI initiatives in the public sector. The study further examined the specific applications of AI initiatives within Post-NPM agendas. Our findings provide some empirical evidence for a better understanding of the conditions and where to target AI-based solutions in post-NPM context for positive outcomes.

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  • Collaborating with employees on service design alleviates concerns of AI replacing jobs and enhances internal use and adoption.

    “Collaborating with employees on service design alleviates concerns of AI replacing jobs and enhances internal use and adoption”

    From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as A. Ojo et al., 2019

    Section 4.2 (p. 9) identifies a need to reassure employees that AI will support their jobs and that they remain needed, while also addressing employee adoption. It does not say that collaborating with employees on service design alleviates those concerns or increases internal use and adoption. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Citizen collaboration (duty values) helps with equality and inclusiveness.

    “citizen collaboration (duty values) helps with equality and inclusiveness”

    From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Ojo et al., 2019

    Section 2.1 (p. 3) identifies citizen engagement and co-production with citizens as aspects of collaboration. It does not describe these as “duty values” or say that citizen collaboration helps achieve equality or inclusiveness. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • For complex interdependent problems, AI-augmented decision-making uncovers new options, anomaly detection, rigorous risk identification, and better service planning and interventions.

    “For complex interdependent problems, AI-augmented decision-making uncovers new options, anomaly detection, rigorous risk identification, and better service planning and interventions”

    From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as A. Ojo et al., 2019

    Section 4.2 (p. 9) lists better decision-making and anomaly detection, more effective risk identification, and better service planning and intervention as outcomes of AI initiatives. Section 2.1 (p. 3) discusses interdependent actors in public-service delivery, but the copy does not link these AI outcomes specifically to complex interdependent problems or say that AI uncovers new options. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • AI systems are prone to failures and malfunctions from time to time learning negative behaviour from the environment.

    “AI systems are prone to failures and malfunctions from time to time learning negative behaviour from the environment”

    From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as A. Ojo et al., 2019

    Section 4.1.3 (p. 6) says AI systems produce poor predictive results and fail from time to time; the challenges table in section 4.2 (p. 9) also lists intermittent failures. Neither passage supports the added claim that AI systems learn negative behaviour from the environment. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • However, the drawback of increased transparency is the ability to game the system for private motives.

    “However, the drawback of increased transparency is the ability to game the system for private motives”

    From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Ojo et al., 2019

    Section 2.1 (p. 2) describes transparency initiatives as providing access to government information, but does not discuss people gaming the system for private motives. Section 4.1 (p. 6) lists challenges of public-sector AI without identifying that drawback of transparency. Searches for gaming, manipulation, and exploitation found no passage addressing the claim. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 6

  1. [13][13] G. Stoker, 'Public Value Management: A New Narrative for Networked Governance?,' Am. Rev. Public Adm. Vol. , vol. 36, no. 1, pp. 41-57, 2006. -
  2. [25][25] a H. W. Chun, 'Using AI for e-Government automatic assessment of immigration application forms,' Proc. Natl. Conf. Artif. Intell. , vol. 2, pp. 16841691, 2007. -
  3. [27][27] M. Madaio, O. L. Haimson, W. Zhang, X. Chang, and M. Hinds-aldrich, 'Identifying and Prioritizing Fire Inspections : A Case Study of Predicting Fire Risk in Atlanta,' in Bloomberg Data for Good Exchange , 2015, pp. 0-5. -
  4. [32][32] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, 'Management Information Quarterly,' vol. 27, no. 3, pp. 425-478, 2003. -
  5. [33][33] W. DeLone and E. McLean, 'The DeLone and McLean Model of Information Systems Success,' J. Manag. Inf. Syst. , vol. 19, no. 4, pp. 9-30, 2003. -
  6. [35][35] A. Burton-Jones, B. Butler, S. Scott, and S. X. Xu, 'Next-Generation Information Systems Theories Special Issue Editors Motivation for the Special Issue,' no. March 2018, pp. 1-5, 2019.

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Authors
Ojo, A., Mellouli, S., & Ahmadi Zeleti, F.
Affiliations
  • Ollscoil na Gaillimhe – University of Galway (IE)
  • Université Laval (CA)
  • Insight Centre for Data Analytics
ORCID
Venue
Proceedings of the 20th Annual International Conference on Digital Government Research
Publisher
ACM
Published
2019-06-18
Pages
159-170
ISSN
3050-8681 (ISSN-L)
DOI
10.1145/3325112.3325261
OpenAlex
W2953383812
Cited by
63
References
22
Citations by year
2026: 4 · 2025: 14 · 2024: 17 · 2023: 12 · 2022: 11 · 2021: 4 · 2019: 1
FWCI
27.57
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • Annual International Conference on Digital Government Research (publishedVersion)
  • Maynooth University ePrints and eTheses Archive (Maynooth University) (acceptedVersion)
Funders
  • Science Foundation Ireland (SFI/12/RC/, SFI/12/RC/2289., 12/RC/, Grant Number SFI/12/RC/2289, 12/RC/2289)
  • European Regional Development Fund (SFI/12/RC/2289)
Topics
  • Social Sciences › Political Science and International Relations › E-Government and Public Services
  • Social Sciences › Public Administration › Public Policy and Administration Research
  • Social Sciences › Political Science and International Relations › Public Administration and Political Analysis
Keywords
artificial intelligence in public administration, New Public Management, digital era governance, public value management, new public governance, performance governance, public sector innovation, citizen participation

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