Artificial intelligence implementation in manufacturing SMEs : A resource orchestration approach

dc.contributor.authorPeretz-Andersson, Einav
dc.contributor.authorTabares, Sabrina
dc.contributor.authorMikalef, Patrick
dc.contributor.authorParida, Vinit
dc.contributor.departmentfi=Ei tutkimusalustaa|en=No platform|-
dc.contributor.facultyfi=Johtamisen yksikkö|en=School of Management|-
dc.contributor.orcidhttps://orcid.org/0000-0003-3255-414X-
dc.contributor.organizationfi=Vaasan yliopisto|en=University of Vaasa|
dc.date.accessioned2024-04-22T10:42:34Z
dc.date.accessioned2025-06-25T13:26:04Z
dc.date.available2024-04-22T10:42:34Z
dc.date.issued2024-04-03
dc.description.abstractArtificial intelligence (AI) is playing a leading role in the digital transformation of enterprises, particularly in the manufacturing industry where it has been responsible for a profound transformation in key business and production operations. Despite the accelerated growth of AI technologies, knowledge of the implementation of AI by small and medium-sized enterprises (SMEs) remains underexplored. Thus, this study seeks to examine how manufacturing SMEs orchestrate resources for AI implementation. Building on the resource orchestration (RO) theory and recent work on AI implementation, we investigate multiple case studies involving manufacturing SMEs in Sweden operating in the packaging, plastic, and metal sectors. Our findings indicate that SMEs structure a portfolio based on acquiring and accumulating AI resources. AI resources are bundled into learning and governance capabilities to leverage configurations for AI implementation. Through a dynamic process of AI resource orchestration, SMEs effectively leverage AI resources and capabilities by mobilising technologies, coordinating manufacturing processes, and empowering skilled people. This research contributes to existing practice and the academic literature on AI implementation, highlighting how SMEs orchestrate AI resources and capabilities to drive an organisation’s digital transformation whilst creating a competitive advantage.-
dc.description.notification© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)-
dc.description.reviewstatusfi=vertaisarvioitu|en=peerReviewed|-
dc.format.bitstreamtrue
dc.format.contentfi=kokoteksti|en=fulltext|-
dc.format.extent23-
dc.identifier.olddbid20426
dc.identifier.oldhandle10024/17188
dc.identifier.urihttps://osuva.uwasa.fi/handle/11111/2097
dc.identifier.urnURN:NBN:fi-fe2024042220098-
dc.language.isoeng-
dc.publisherElsevier-
dc.relation.doi10.1016/j.ijinfomgt.2024.102781-
dc.relation.ispartofjournalInternational Journal of Information Management-
dc.relation.issn1873-4707-
dc.relation.issn0268-4012-
dc.relation.urlhttps://doi.org/10.1016/j.ijinfomgt.2024.102781-
dc.relation.volume77-
dc.rightsCC BY 4.0-
dc.source.identifierScopus:85189496050-
dc.source.identifierhttps://osuva.uwasa.fi/handle/10024/17188
dc.subjectAI-
dc.subjectArtificial intelligence-
dc.subjectCapabilities-
dc.subjectCompetitive advantage-
dc.subjectDigital transformation-
dc.subjectManufacturing-
dc.subjectResources-
dc.subjectSmall and medium-sized enterprises (SMEs)-
dc.subject.disciplinefi=Strateginen johtaminen|en=Strategic Management|-
dc.titleArtificial intelligence implementation in manufacturing SMEs : A resource orchestration approach-
dc.type.okmfi=A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä|en=A1 Peer-reviewed original journal article|sv=A1 Originalartikel i en vetenskaplig tidskrift|-
dc.type.publicationarticle-
dc.type.versionpublishedVersion-

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