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

By Joseph Hair, Marcelo L. D. S. Gabriel, V. Patel. 2014

1,091 citations recorded by the source corpus.

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.

Subfields: Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/ada65abeff666184c7d77f1af3f366262a5d860f

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Citations in the corpus

• Factors influencing autonomous vehicle adoption: an application of the technology acceptance model and innovation diffusion theory (2020, positive): “According to the criteria recommended by Hair Jr, Gabriel, and Patel (2014), root mean square error of approximation (RMSEA) and standardised root mean square residual (SRMR) values should be less than 0.08 and 0.06.”