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Modeling indices using partial least squares

2023-12-01Artigo científico Acesso aberto
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dc.contributor.authorDi̇rsehan, Taşkın
dc.contributor.authorHenseler, Jörg
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblSpringer Science Business Media
dc.date.accessioned2022-11-02T22:11:05Z
dc.date.available2022-11-02T22:11:05Z
dc.date.issued2023-12-01
dc.descriptionDi̇rsehan, T., & Henseler, J. (2023). Modeling indices using partial least squares: How to determine the optimum weights? Quality & Quantity, 57(4 Advanced PLS-PM Applications in Social Sciences), 521-535. https://doi.org/10.1007/s11135-022-01515-5 ---- Funding: Jörg Henseler gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through a research grant from the Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020).
dc.description.abstractIndices are often used to model theoretical concepts in economics and finance. Beyond the econometric models used to test the relationships between these variables, partial least squares path modeling (PLS-PM) allows the study of complex models, but it is an estimator that is still in its infancy in economics and finance research. Thus, the use of PLS-PM for composite analysis needs to be explored further. As one such attempt, this paper is focused on the determination of the indices’ optimum weights. For this purpose, the effects of the market potential index (MPI) on foreign direct investment (FDI) and gross domestic product (GDP) were analysed by implementing different weighting schemes. The assessment of the model shows that PLS Mode B leads to better model fit.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent15
dc.format.extent1192302
dc.identifier.doi10.1007/s11135-022-01515-5
dc.identifier.issn0033-5177
dc.identifier.otherPURE: 47525364
dc.identifier.otherPURE UUID: 5d99357c-bbfd-41e2-bc46-e7e1800edd38
dc.identifier.othercrossref: 10.1007/s11135-022-01515-5
dc.identifier.otherScopus: 85140984630
dc.identifier.urihttp://hdl.handle.net/10362/145169
dc.identifier.urlhttps://www.scopus.com/pages/publications/85140984630
dc.identifier.urlhttps://link.springer.com/10.1007/s11135-022-01515-5
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectPLS Mode A
dc.subjectPLS Mode B
dc.subjectPartial least squares path modeling
dc.subjectComposite measurement
dc.subjectIndices
dc.subjectWeighting schemes
dc.subjectStatistics and Probability
dc.subjectGeneral Social Sciences
dc.subjectSDG 8 - Decent Work and Economic Growth
dc.subjectSDG 10 - Reduced Inequalities
dc.titleModeling indices using partial least squaresen
dc.title.subtitleHow to determine the optimum weights?en
dc.typejournal article
degois.publication.firstPage521
degois.publication.issue4 Advanced PLS-PM Applications in Social Sciences
degois.publication.lastPage535
degois.publication.titleQuality & Quantity
degois.publication.volume57
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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