INTRODUCTION Structural Equation Modeling (SEM) is a comprehensive statistical method to test hypotheses about causal relationships between the observed and unobserved (latent used) variables and proved to be useful for solving problems in the formulation of theoretical constructs (Reisinger, 1999) . Its function is found better than other multivariate statistical techniques, multiple regression, root cause analysis and factor analysis. Other statistical techniques could not be considered because the effects depend on, and the interaction between the independent variables. Therefore, a method that can be a series of reports examining the dependence simultaneously address complex issues of management and conduct. SEM can also extend the significance and statistical power for examining the model with a unique and complete (Pang, 1996). Steenkamp and Baumgartner (2000) on the role of SEM in marketing decision modeling and decision management. They discuss the benefits of IT. They said that although SEM has the potential for modeling decision support, it is probably preferable to the theory that an important phase in the development of marketing models [for the SEM and LISREL see test; Byrne (1998), Cheng (2001), Cudeck et al. (200), Hayduk (1987), J
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