Modeling indices using partial least squares

Detalhes bibliográficos
Autor(a) principal: Di̇rsehan, Taşkın
Data de Publicação: 2022
Outros Autores: Henseler, Jörg
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/145169
Resumo: Di̇rsehan, 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).
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spelling Modeling indices using partial least squaresHow to determine the optimum weights?PLS Mode APLS Mode BPartial least squares path modelingComposite measurementIndicesWeighting schemesStatistics and ProbabilitySocial Sciences(all)SDG 8 - Decent Work and Economic GrowthSDG 10 - Reduced InequalitiesDi̇rsehan, 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).Indices 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.Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNDi̇rsehan, TaşkınHenseler, Jörg2022-11-02T22:11:05Z2023-12-012023-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article15application/pdfhttp://hdl.handle.net/10362/145169eng0033-5177PURE: 47525364https://doi.org/10.1007/s11135-022-01515-5info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:25:18Zoai:run.unl.pt:10362/145169Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:51:56.131978Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Modeling indices using partial least squares
How to determine the optimum weights?
title Modeling indices using partial least squares
spellingShingle Modeling indices using partial least squares
Di̇rsehan, Taşkın
PLS Mode A
PLS Mode B
Partial least squares path modeling
Composite measurement
Indices
Weighting schemes
Statistics and Probability
Social Sciences(all)
SDG 8 - Decent Work and Economic Growth
SDG 10 - Reduced Inequalities
title_short Modeling indices using partial least squares
title_full Modeling indices using partial least squares
title_fullStr Modeling indices using partial least squares
title_full_unstemmed Modeling indices using partial least squares
title_sort Modeling indices using partial least squares
author Di̇rsehan, Taşkın
author_facet Di̇rsehan, Taşkın
Henseler, Jörg
author_role author
author2 Henseler, Jörg
author2_role author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Di̇rsehan, Taşkın
Henseler, Jörg
dc.subject.por.fl_str_mv PLS Mode A
PLS Mode B
Partial least squares path modeling
Composite measurement
Indices
Weighting schemes
Statistics and Probability
Social Sciences(all)
SDG 8 - Decent Work and Economic Growth
SDG 10 - Reduced Inequalities
topic PLS Mode A
PLS Mode B
Partial least squares path modeling
Composite measurement
Indices
Weighting schemes
Statistics and Probability
Social Sciences(all)
SDG 8 - Decent Work and Economic Growth
SDG 10 - Reduced Inequalities
description Di̇rsehan, 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).
publishDate 2022
dc.date.none.fl_str_mv 2022-11-02T22:11:05Z
2023-12-01
2023-12-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/145169
url http://hdl.handle.net/10362/145169
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0033-5177
PURE: 47525364
https://doi.org/10.1007/s11135-022-01515-5
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 15
application/pdf
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instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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