AMOS Covariance-Based Structural Equation Modeling (CB-SEM): Guidelines on Its Application as a Marketing Research Tool

J. Hair, M. Gabriel, V. Patel
{"title":"AMOS Covariance-Based Structural Equation Modeling (CB-SEM): Guidelines on Its Application as a Marketing Research Tool","authors":"J. Hair, M. Gabriel, V. Patel","doi":"10.5585/REMARK.V13I2.2718","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"503","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Model Construction & Estimation (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5585/REMARK.V13I2.2718","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 503

Abstract

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.
基于协方差的结构方程模型(CB-SEM):作为营销研究工具的应用指南
结构方程建模(SEM)越来越成为社会科学,特别是市场营销学科中概念和理论发展的首选方法。在市场营销研究中,越来越需要评估复杂的多重潜在结构和关系。二阶结构可以建模,提供对关系的改进的理论理解以及简化。SEM特别适合于研究多个结构之间的复杂关系。两种最流行的基于扫描电镜的分析方法是基于协方差的扫描电镜(CB-SEM)和基于方差的扫描电镜(PLS-SEM)。虽然每种技术都有其优点和局限性,但在本文中,我们将重点介绍使用AMOS的CB-SEM,以说明其在检查客户导向、员工导向和公司绩效之间关系方面的应用。我们还演示了高阶结构在为客户导向和员工导向的响应性和主动性组件建模时是如何有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信