Partial Least Squares is an Estimator for Structural Equation Models

Detalhes bibliográficos
Autor(a) principal: Schuberth, Florian
Data de Publicação: 2023
Outros Autores: Zaza, Sam, 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/10362/154668
Resumo: Schuberth, F., Zaza, S., & Henseler, J. (2023). Partial Least Squares is an Estimator for Structural Equation Models: A Comment on Evermann and Rönkkö (2021). Communications of the Association for Information Systems, 52, 711-729. https://doi.org/10.17705/1CAIS.05232 --- Funding Information: [*Note: Jörg Henseler acknowledges a financial interest in the composite-based SEM software ADANCO and its distributor, Composite Modeling. Moreover, he gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020).]
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spelling Partial Least Squares is an Estimator for Structural Equation ModelsA Comment on Evermann and Rönkkö (2021)Confirmatory Composite AnalysisDiscriminant ValidityEmergent VariablesGuidelinesHenseler-Ogasawara SpecificationNew DevelopmentsPartial Least SquaresStructural Equation ModelingSchuberth, F., Zaza, S., & Henseler, J. (2023). Partial Least Squares is an Estimator for Structural Equation Models: A Comment on Evermann and Rönkkö (2021). Communications of the Association for Information Systems, 52, 711-729. https://doi.org/10.17705/1CAIS.05232 --- Funding Information: [*Note: Jörg Henseler acknowledges a financial interest in the composite-based SEM software ADANCO and its distributor, Composite Modeling. Moreover, he gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020).]In 2012 and 2013, several critical publications questioned many alleged PLS properties. As a consequence, PLS benefited from a boost of developments. It is, therefore, a good time to review these developments. Evermann and Rönkkö (2023) devote their paper to this task and formulate guidelines in the form of 14 recommendations. Yet, while they identified the major developments, they overlook a fundamental change, maybe because it is so subtle: the view on PLS. As mentioned by Evermann and Rönkkö (2023, p. 1), “[PLS] is a statistical method used to estimate linear structural equation models” and consequently should not be regarded as a standalone SEM technique following its own assessment criteria. Against this background, we explain which models can be estimated by PLS and PLSc. Moreover, we present the Henseler-Ogasawara specification to estimate composite models by common SEM estimators. Additionally, we review Evermann and Rönkkö’s (2023) 14 recommendations one by one and suggest updates and improvements where necessary. Further, we address their comments about the latest advancement in composite models and show that PLS is a viable estimator for confirmatory composite analysis. Finally, we conclude that there is little value in distinguishing between covariance-based and variance-based SEM—there is only SEM.Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNSchuberth, FlorianZaza, SamHenseler, Jörg2023-06-30T22:16:06Z2023-062023-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article19application/pdfhttp://hdl.handle.net/10362/154668eng1529-3181PURE: 65002168https://doi.org/10.17705/1CAIS.05232info: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:RCAAP2024-03-11T05:37:08Zoai:run.unl.pt:10362/154668Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:43.213859Repositório 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 Partial Least Squares is an Estimator for Structural Equation Models
A Comment on Evermann and Rönkkö (2021)
title Partial Least Squares is an Estimator for Structural Equation Models
spellingShingle Partial Least Squares is an Estimator for Structural Equation Models
Schuberth, Florian
Confirmatory Composite Analysis
Discriminant Validity
Emergent Variables
Guidelines
Henseler-Ogasawara Specification
New Developments
Partial Least Squares
Structural Equation Modeling
title_short Partial Least Squares is an Estimator for Structural Equation Models
title_full Partial Least Squares is an Estimator for Structural Equation Models
title_fullStr Partial Least Squares is an Estimator for Structural Equation Models
title_full_unstemmed Partial Least Squares is an Estimator for Structural Equation Models
title_sort Partial Least Squares is an Estimator for Structural Equation Models
author Schuberth, Florian
author_facet Schuberth, Florian
Zaza, Sam
Henseler, Jörg
author_role author
author2 Zaza, Sam
Henseler, Jörg
author2_role author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Schuberth, Florian
Zaza, Sam
Henseler, Jörg
dc.subject.por.fl_str_mv Confirmatory Composite Analysis
Discriminant Validity
Emergent Variables
Guidelines
Henseler-Ogasawara Specification
New Developments
Partial Least Squares
Structural Equation Modeling
topic Confirmatory Composite Analysis
Discriminant Validity
Emergent Variables
Guidelines
Henseler-Ogasawara Specification
New Developments
Partial Least Squares
Structural Equation Modeling
description Schuberth, F., Zaza, S., & Henseler, J. (2023). Partial Least Squares is an Estimator for Structural Equation Models: A Comment on Evermann and Rönkkö (2021). Communications of the Association for Information Systems, 52, 711-729. https://doi.org/10.17705/1CAIS.05232 --- Funding Information: [*Note: Jörg Henseler acknowledges a financial interest in the composite-based SEM software ADANCO and its distributor, Composite Modeling. Moreover, he gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020).]
publishDate 2023
dc.date.none.fl_str_mv 2023-06-30T22:16:06Z
2023-06
2023-06-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/154668
url http://hdl.handle.net/10362/154668
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1529-3181
PURE: 65002168
https://doi.org/10.17705/1CAIS.05232
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