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Machine learning approaches to understand IT outsourcing portfolios

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dc.contributor.authorRavindran, Kiron
dc.contributor.authorLu, Yingda
dc.contributor.authorSusarla, Anjana
dc.contributor.authorMani, Deepa
dc.contributor.rorhttps://ror.org/02jjdwm75
dc.date.accessioned2025-05-28T15:14:05Z
dc.date.available2025-05-28T15:14:05Z
dc.date.issued2023-01-10
dc.description.abstractThe outsourcing of IT services poses a conundrum to the traditional theories of the firm. While there are many prescriptive sourcing metrics that are geared towards the evaluation of tangible and measurable aspects of vendors and clients, much of the information that is traditionally important in making such decisions is unstructured. To address this challenge, we train and apply our own NLP model based on deep learning methods using doc2vec, which allows users to create semi-supervised methods for representation of words. We find two novel constructs, vendor–client alignment and vendor–task alignment, that shape partner selection and the alternatives faced by clients in IT outsourcing, as opposed to agency or transaction cost considerations alone. Our method suggests that NLP and machine learning approaches provide additional insight, over and above traditionally understood variables in academic literature and trade and industry press, about the difficult-to-elicit aspects of vendor–client interaction.
dc.description.peerreviewedyes
dc.description.statusPublished
dc.formatapplication/pdf
dc.identifier.citationLu, Y., Susarla, A., Ravindran, K., & Mani, D. (2024). Machine learning approaches to understand IT outsourcing portfolios. Electronic Commerce Research, 24(4), 2547-2577.https://doi.org/10.1007/s10660-022-09663-4
dc.identifier.doihttps://doi.org/10.1007/s10660-022-09663-4
dc.identifier.issn1389-5753
dc.identifier.urihttps://hdl.handle.net/20.500.14417/3799
dc.issue.number4
dc.journal.titleElectronic Commerce Research
dc.language.isoen
dc.page.final2577
dc.page.initial2547
dc.page.total42
dc.publisherSpringer
dc.relation.departmentInformation Systems & Technology
dc.relation.entityIE University
dc.relation.schoolIE Business School
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.en
dc.titleMachine learning approaches to understand IT outsourcing portfolios
dc.typeinfo:eu-repo/semantics/article
dc.version.typeinfo:eu-repo/semantics/acceptedVersion
dc.volume.number24
dspace.entity.typePublication
relation.isAuthorOfPublicationec436d63-dae4-423d-9355-3e46fe8640cc
relation.isAuthorOfPublication.latestForDiscoveryec436d63-dae4-423d-9355-3e46fe8640cc
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