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dc.contributor.authorYu, Y
dc.contributor.authorXu, J
dc.contributor.authorZhang, JZ
dc.contributor.authorLiu, Y
dc.contributor.authorKamal, MM
dc.contributor.authorCao, Y
dc.date.accessioned2024-04-15T12:37:11Z
dc.date.issued2024-02-19
dc.date.updated2024-04-08T17:16:13Z
dc.description.abstractThe empowerment of Artificial Intelligence (AI) in manufacturing has drawn considerable attention, yet a holistic understanding of AI's effects on manufacturers' resilience and performance remains elusive. Our research leverages organizational information processing theory to explore the impact of three AI types – cognitive insights, process automation, and cognitive engagement – on manufacturers' resilience and performance. Our findings unveil that AI-driven process automation and cognitive engagement significantly influence both planned and adaptive resilience among manufacturers, while AI for cognitive insights predominantly elevates planned resilience without substantial effects on adaptive resilience. Moreover, our study establishes a positive link between planned and adaptive resilience and manufacturers' operational performance. By enhancing the comprehension of AI's implications for organizational resilience, our research yields crucial managerial insights and fresh perspectives for industry practitioners.en_GB
dc.format.extent109175-
dc.identifier.citationVol. 270, article 109175en_GB
dc.identifier.doihttps://doi.org/10.1016/j.ijpe.2024.109175
dc.identifier.urihttp://hdl.handle.net/10871/135747
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.rights.embargoreasonUnder embargo until 19 July 2025 in compliance with publisher policyen_GB
dc.rights© 2024. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0en_GB
dc.titleUnleashing the power of AI in manufacturing: Enhancing resilience and performance through cognitive insights, process automation, and cognitive engagementen_GB
dc.typeArticleen_GB
dc.date.available2024-04-15T12:37:11Z
dc.identifier.issn0925-5273
exeter.article-number109175
dc.descriptionThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record en_GB
dc.identifier.eissn1873-7579
dc.identifier.journalInternational Journal of Production Economicsen_GB
dc.relation.ispartofInternational Journal of Production Economics, 270
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_GB
dcterms.dateAccepted2024-02-03
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2024-02-19
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2024-04-15T12:32:52Z
refterms.versionFCDAM
refterms.panelCen_GB
refterms.dateFirstOnline2024-02-19


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© 2024. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0
Except where otherwise noted, this item's licence is described as © 2024. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0