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Cited by 106E-Government and Public ServicesInformation Technology Governance and StrategyPublic Policy and Administration Research

The DOI matches its Crossref record; 141 of the 144 references it cites were found in a registry or an index.

An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia

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Alshahrani, Albandari, Dennehy, Denis, Mäntymäki, Matti·2022·Government Information Quarterly
DOI 10.1016/j.giq.2021.101617
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Abstract

Artificial Intelligence (AI) has been suggested to have transformative potential for public sector organizations through enabling increased productivity and novel ways to deliver public services. In order to materialize the transformative potential of AI, public sector organizations need to successfully assimilate AI in their operational activities. However, AI assimilation in the public sector appears to be fragmented and lagging the private sector, and the phenomena has really limited attention from academic research community. To address this gap, we adopt the case study approach to explore three Saudi-Arabian public sector organizations and analyze the results using the attention-based view of the organization (ABV) as the theoretical lens. This study elucidates the challenges related AI assimilation in public sector in terms of how organizational attention is focused situated and distributed during the assimilation process. Five key challenges emerged from the cases studied, namely (i) misalignment between AI and management decision-making, (ii) tensions with linguistics and national culture, (iii) developing and implementing AI infrastructure, (iv) data integrity and sharing, and (v) ethical and governance concerns. The findings reveal a re-enforcing relationship between the situated attention and structural distribution of attention that can accelerate the successful assimilation of AI in public sector organizations.

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Cites

Research works printed but not linked: 7

  1. Allen, 2019, Understanding China’s AI strategy: Clues to Chinese strategic thinking on artificial intelligence and National Security, Center for a New American Security, 1
  2. Alwalidi, 2010, Making E-l invisible: Experience king Khalid Saudi Arabia, Educational Technology, 50, 4
  3. Andreasson, 2019, Nordic municipalities’ work with artificial intelligence, Nordic municipalities’ work with artificial intelligence, Nordic Council of Ministers, 2019
  4. Eggers, 2017, AI-augmented government. Using cognitive technologies to redesign public sector work, Deloitte Center for Government Insights, 1
  5. Ransbotham, 2018, Artificial intelligence in business gets real, MIT Sloan Management Review, 2
  6. Weibl, 2018, Success or failure of big data: Insights of managerial challenges from a technology assimilation perspective, Proceedings of the Multikonferenz Wirtschaftsinformatik (MKWI), 12
  7. Wilson, 2018, Collaborative intelligence: Humans and AI are joining forces, Harvard Business Review, 96, 114

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Metadata

Authors
Alshahrani, A., Dennehy, D., & Mäntymäki, M.
Affiliations
  • Ollscoil na Gaillimhe – University of Galway (IE)
  • University of Turku (FI)
Corresponding author
Dennehy, Denis
ORCID
Venue
Government Information Quarterly
Publisher
Elsevier BV
Published
2022-10
Volume
39
Issue
4
Pages
101617
ISSN
0740-624X (ISSN-L)
DOI
10.1016/j.giq.2021.101617
OpenAlex
W3186485936
Cited by
106
References
144
Citations by year
2026: 23 · 2025: 31 · 2024: 13 · 2023: 22 · 2022: 15
FWCI
43.20
Type
journal article
Language
en
Open access
Licence
Locations
  • Government Information Quarterly (publishedVersion, cc-by)
  • UTUPub (University of Turku) (submittedVersion)
Funders
Taif University
Topics
  • Social Sciences › Political Science and International Relations › E-Government and Public Services
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
  • Social Sciences › Public Administration › Public Policy and Administration Research
Keywords
public sector organizations, attention-based view, distributed attention, AI adoption barriers, management decision-making, national culture, AI infrastructure, AI ethics, AI governance, Saudi Arabia

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