Amos Covariance-Based Structural Equation Modeling (Cb-Sem): Guidelines on its Application as a Marketing Research Tool
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | REMark - Revista Brasileira de Marketing |
Texto Completo: | https://periodicos.uninove.br/remark/article/view/12031 |
Resumo: | Structural equation modeling (SEM) is increasingly a method of choice for concept and theory development in the social sciences, particularly the marketing discipline. In marketing research there increasingly is a need to assess complex multiple latent constructs and relationships. Second-order constructs can be modeled providing an improved theoretical understanding of relationships as well as parsimony. SEM in particular is well suited to investigating complex relationships among multiple constructs. The two most prevalent SEM based analytical methods are covariance-based SEM (CB-SEM) and variance-based SEM (PLS-SEM). While each technique has advantages and limitations, in this article we focus on CB-SEM with AMOS to illustrate its application in examining the relationships between customer orientation, employee orientation, and firm performance. We also demonstrate how higher-order constructs are useful in modeling both responsive and proactive components of customer and employee orientation. |
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Amos Covariance-Based Structural Equation Modeling (Cb-Sem): Guidelines on its Application as a Marketing Research ToolModelagem de Equações Estruturais Baseada em Covariância (CB-SEM) com o AMOS: Orientações sobre a sua aplicação como uma Ferramenta de Pesquisa de MarketingStructural Equation Modeling (SEM); Covariance-Based SEM; AMOS; Marketing Research.Modelagem de Equações Estruturais (SEM), baseado em covariância SEM, AMOS, Pesquisa de Marketing.Structural equation modeling (SEM) is increasingly a method of choice for concept and theory development in the social sciences, particularly the marketing discipline. In marketing research there increasingly is a need to assess complex multiple latent constructs and relationships. Second-order constructs can be modeled providing an improved theoretical understanding of relationships as well as parsimony. SEM in particular is well suited to investigating complex relationships among multiple constructs. The two most prevalent SEM based analytical methods are covariance-based SEM (CB-SEM) and variance-based SEM (PLS-SEM). While each technique has advantages and limitations, in this article we focus on CB-SEM with AMOS to illustrate its application in examining the relationships between customer orientation, employee orientation, and firm performance. We also demonstrate how higher-order constructs are useful in modeling both responsive and proactive components of customer and employee orientation.A modelagem de equaes estruturais (Structural Equation Modeling -SEM) cada vez mais usada como um mtodo para a conceituao e desenvolvimento de aspectos tericos nas cincias sociais aplicadas, em particular na rea de marketing, pois mais e mais h a necessidade de avaliar vrios constructos e relaes latentes complexas. Tambm, constructos de segunda ordem podem ser modelados fornecendo uma melhor compreenso terica de relaes com boa parcimnia. Modelagens do tipo SEM so, em particular, bem adequadas para investigar as relaes complexas entre os vrios constructos. Os dois mtodos analticos SEM mais prevalentes so os baseados em covarincia SEM (CB-SEM) e os baseados em varincia SEM (PLS-SEM). Embora cada tcnica tenha suas vantagens e limitaes, neste artigo vamos nos concentrar no CB-SEM com o AMOS para ilustrar sua aplicao na anlise das relaes entre orientao para o cliente, a orientao para os funcionrios e desempenho da empresa. Tambm ser demonstrado como constructos de segunda ordem so teis para modelar os componentes de pr-atividade e responsividade dessas relaes.DOI: 10.5585/remark.v13i2.2718Universidade Nove de Julho - Uninove2014-05-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAvaliado por ParesPeer-reviewed Articleapplication/pdfapplication/pdfhttps://periodicos.uninove.br/remark/article/view/1203110.5585/remark.v13i2.2718ReMark - Revista Brasileira de Marketing; v. 13, n. 2 (2014): Edição Especial; 44-552177-5184reponame:REMark - Revista Brasileira de Marketinginstname:Universidade Nove de Julho (UNINOVE)instacron:RBMporhttps://periodicos.uninove.br/remark/article/view/12031/5662https://periodicos.uninove.br/remark/article/view/12031/5663Direitos autorais 2019 Revista Brasileira de Marketing – Remarkinfo:eu-repo/semantics/openAccessHair Jr., Joseph F.Gabriel, Marcelo Luiz Dias da SilvaPatel, Vijay K.2019-02-19T17:43:54Zoai:https://periodicos.uninove.br:article/12031Revistahttps://periodicos.uninove.br/remarkPRIhttps://periodicos.uninove.br/remark/oaiclaudiaraac@uol.com.br || admin@revistabrasileiramarketing.org || admin@revistabrasileiramarketing.org2177-51842177-5184opendoar:2019-02-19T17:43:54REMark - Revista Brasileira de Marketing - Universidade Nove de Julho (UNINOVE)false |
dc.title.none.fl_str_mv |
Amos Covariance-Based Structural Equation Modeling (Cb-Sem): Guidelines on its Application as a Marketing Research Tool Modelagem de Equações Estruturais Baseada em Covariância (CB-SEM) com o AMOS: Orientações sobre a sua aplicação como uma Ferramenta de Pesquisa de Marketing |
title |
Amos Covariance-Based Structural Equation Modeling (Cb-Sem): Guidelines on its Application as a Marketing Research Tool |
spellingShingle |
Amos Covariance-Based Structural Equation Modeling (Cb-Sem): Guidelines on its Application as a Marketing Research Tool Hair Jr., Joseph F. Structural Equation Modeling (SEM); Covariance-Based SEM; AMOS; Marketing Research. Modelagem de Equações Estruturais (SEM), baseado em covariância SEM, AMOS, Pesquisa de Marketing. |
title_short |
Amos Covariance-Based Structural Equation Modeling (Cb-Sem): Guidelines on its Application as a Marketing Research Tool |
title_full |
Amos Covariance-Based Structural Equation Modeling (Cb-Sem): Guidelines on its Application as a Marketing Research Tool |
title_fullStr |
Amos Covariance-Based Structural Equation Modeling (Cb-Sem): Guidelines on its Application as a Marketing Research Tool |
title_full_unstemmed |
Amos Covariance-Based Structural Equation Modeling (Cb-Sem): Guidelines on its Application as a Marketing Research Tool |
title_sort |
Amos Covariance-Based Structural Equation Modeling (Cb-Sem): Guidelines on its Application as a Marketing Research Tool |
author |
Hair Jr., Joseph F. |
author_facet |
Hair Jr., Joseph F. Gabriel, Marcelo Luiz Dias da Silva Patel, Vijay K. |
author_role |
author |
author2 |
Gabriel, Marcelo Luiz Dias da Silva Patel, Vijay K. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Hair Jr., Joseph F. Gabriel, Marcelo Luiz Dias da Silva Patel, Vijay K. |
dc.subject.por.fl_str_mv |
Structural Equation Modeling (SEM); Covariance-Based SEM; AMOS; Marketing Research. Modelagem de Equações Estruturais (SEM), baseado em covariância SEM, AMOS, Pesquisa de Marketing. |
topic |
Structural Equation Modeling (SEM); Covariance-Based SEM; AMOS; Marketing Research. Modelagem de Equações Estruturais (SEM), baseado em covariância SEM, AMOS, Pesquisa de Marketing. |
description |
Structural equation modeling (SEM) is increasingly a method of choice for concept and theory development in the social sciences, particularly the marketing discipline. In marketing research there increasingly is a need to assess complex multiple latent constructs and relationships. Second-order constructs can be modeled providing an improved theoretical understanding of relationships as well as parsimony. SEM in particular is well suited to investigating complex relationships among multiple constructs. The two most prevalent SEM based analytical methods are covariance-based SEM (CB-SEM) and variance-based SEM (PLS-SEM). While each technique has advantages and limitations, in this article we focus on CB-SEM with AMOS to illustrate its application in examining the relationships between customer orientation, employee orientation, and firm performance. We also demonstrate how higher-order constructs are useful in modeling both responsive and proactive components of customer and employee orientation. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-05-23 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Avaliado por Pares Peer-reviewed Article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.uninove.br/remark/article/view/12031 10.5585/remark.v13i2.2718 |
url |
https://periodicos.uninove.br/remark/article/view/12031 |
identifier_str_mv |
10.5585/remark.v13i2.2718 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://periodicos.uninove.br/remark/article/view/12031/5662 https://periodicos.uninove.br/remark/article/view/12031/5663 |
dc.rights.driver.fl_str_mv |
Direitos autorais 2019 Revista Brasileira de Marketing – Remark info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Direitos autorais 2019 Revista Brasileira de Marketing – Remark |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Nove de Julho - Uninove |
publisher.none.fl_str_mv |
Universidade Nove de Julho - Uninove |
dc.source.none.fl_str_mv |
ReMark - Revista Brasileira de Marketing; v. 13, n. 2 (2014): Edição Especial; 44-55 2177-5184 reponame:REMark - Revista Brasileira de Marketing instname:Universidade Nove de Julho (UNINOVE) instacron:RBM |
instname_str |
Universidade Nove de Julho (UNINOVE) |
instacron_str |
RBM |
institution |
RBM |
reponame_str |
REMark - Revista Brasileira de Marketing |
collection |
REMark - Revista Brasileira de Marketing |
repository.name.fl_str_mv |
REMark - Revista Brasileira de Marketing - Universidade Nove de Julho (UNINOVE) |
repository.mail.fl_str_mv |
claudiaraac@uol.com.br || admin@revistabrasileiramarketing.org || admin@revistabrasileiramarketing.org |
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1799138639911845888 |