From policy to action: a cross-regulatory conceptual framework to address data and AI regulations

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dc.contributor.authorYu, Fangzhou
dc.contributor.authorCarton, Fergal
dc.contributor.authorXiong, Huanhuan
dc.date.accessioned2026-07-10T09:00:02Z
dc.date.available2026-07-10T09:00:02Z
dc.date.issued2026-05-17
dc.description.abstractOrganisations operating in data- and AI-intensive environments face an increasingly fragmented and overlapping EU regulatory landscape, including the GDPR, AI Act, etc. Existing research largely addresses these regimes in isolation, offering limited guidance on integrated organisational compliance. This study develops a cross-regulatory integration framework that translates multi-regulatory obligations into structured organisational capabilities, explicitly assigning clear actors and responsibilities. Drawing on Actor-Network Theory (ANT) and a Strategic-Tactical-Operational (STO) decision-layer model, this study conceptualises compliance as a networked and multi-level governance process. The framework supports organisations in aligning ethical criteria, technical controls, and governance responsibilities across regulatory domains, advancing enterprise-wide digital governance and compliance management through both operational and functional processes.en
dc.description.versionPublished Version
dc.format.extent10
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid2669329
dc.identifier.authororcidYu, Fangzhou
dc.identifier.authororcidCarton, Fergal
dc.identifier.authororcidXiong, Huanhuan§0000-0002-1809-0388
dc.identifier.citationYu, F, Carton, F & Xiong, H 2026, 'From policy to action: a cross-regulatory conceptual framework to address data and AI regulations', Journal of Decision Systems, vol. 35, no. 1, 2669329, pp. 1-10. https://doi.org/10.1080/12460125.2026.2669329
dc.identifier.doi10.1080/12460125.2026.2669329
dc.identifier.endpage10
dc.identifier.issn1246-0125
dc.identifier.issued1
dc.identifier.journaltitleJournal of Decision Systems
dc.identifier.otherORCID: /0000-0002-1809-0388/work/220336770
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/19046
dc.identifier.volume35
dc.language.isoen
dc.publisherTaylor and Francis Ltd.
dc.relation.urihttps://www.scopus.com/pages/publications/105038958964
dc.rights© 2026, The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allowthe posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
dc.rights.accessrightsopen access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.statusPeer reviewed
dc.subjectActor-Network Theory
dc.subjectAI ACT
dc.subjectCross-regulatory compliance
dc.subjectGDPR
dc.subject[CUBS]
dc.titleFrom policy to action: a cross-regulatory conceptual framework to address data and AI regulationsen
dc.typeArticle (peer-reviewed)
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