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

Albandari Alshahrani, Denis Dennehy Orcid Logo, Matti Mäntymäki

Government Information Quarterly, Volume: 39, Issue: 4, Start page: 101617

Swansea University Author: Denis Dennehy Orcid Logo

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DOI (Published version): 10.1016/j.giq.2021.101617

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 successf...

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Published in: Government Information Quarterly
ISSN: 0740-624X
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa59602
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spelling 2023-01-04T11:22:08.2063267 v2 59602 2022-03-14 An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia ba782cbe94139075e5418dc9274e8304 0000-0001-9931-762X Denis Dennehy Denis Dennehy true false 2022-03-14 BBU 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. Journal Article Government Information Quarterly 39 4 101617 Elsevier BV 0740-624X Artificial intelligence; Decision making; Attention-based view; Public sector 1 7 2021 2021-07-01 10.1016/j.giq.2021.101617 COLLEGE NANME Business COLLEGE CODE BBU Swansea University This research was supported with the financial support that was administered by Saudi Arabian Cultural Bureau (Dublin) on behalf of Taif University, Saudi Arabia. 2023-01-04T11:22:08.2063267 2022-03-14T14:36:06.9118064 Faculty of Humanities and Social Sciences School of Management - Business Management Albandari Alshahrani 1 Denis Dennehy 0000-0001-9931-762X 2 Matti Mäntymäki 3 59602__24263__97dc7ab2096d406a877d46ee694d947f.pdf 59602.pdf 2022-06-09T13:19:18.0847522 Output 2395934 application/pdf Proof true © 2021 The Author(s). This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
title An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia
spellingShingle An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia
Denis Dennehy
title_short An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia
title_full An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia
title_fullStr An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia
title_full_unstemmed An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia
title_sort An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia
author_id_str_mv ba782cbe94139075e5418dc9274e8304
author_id_fullname_str_mv ba782cbe94139075e5418dc9274e8304_***_Denis Dennehy
author Denis Dennehy
author2 Albandari Alshahrani
Denis Dennehy
Matti Mäntymäki
format Journal article
container_title Government Information Quarterly
container_volume 39
container_issue 4
container_start_page 101617
publishDate 2021
institution Swansea University
issn 0740-624X
doi_str_mv 10.1016/j.giq.2021.101617
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description 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.
published_date 2021-07-01T04:17:03Z
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score 11.012678

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