2016
DOI: 10.17705/1cais.03823
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Partial Least Squares Structural Equation Modeling Approach for Analyzing a Model with a Binary Indicator as an Endogenous Variable

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Cited by 29 publications
(18 citation statements)
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“…Studies on LM have been identified to benefit the most from using Structural Equation Modelling (SEM) because SEM is at the stage of explorative modelling with the theory under development (Pearce and Pons, 2019). There are two main approaches to SEM: component-based and covariance-based (Bodoff and Ho, 2016). The example of componentbased SEM is the partial least square (PLS) method, while AMOS is the most well-known software package supporting the covariance-based SEM (Chin, 1998).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Studies on LM have been identified to benefit the most from using Structural Equation Modelling (SEM) because SEM is at the stage of explorative modelling with the theory under development (Pearce and Pons, 2019). There are two main approaches to SEM: component-based and covariance-based (Bodoff and Ho, 2016). The example of componentbased SEM is the partial least square (PLS) method, while AMOS is the most well-known software package supporting the covariance-based SEM (Chin, 1998).…”
Section: Methodsmentioning
confidence: 99%
“…The example of componentbased SEM is the partial least square (PLS) method, while AMOS is the most well-known software package supporting the covariance-based SEM (Chin, 1998). The study by Bodoff and Ho (2016) is referred to in choosing component-based SEM as partial least squares structural equation modelling (PLS-SEM). The PLS-SEM aims to explain variance which allows estimating complex cause-effect relationship models with latent variables using SmartPLS (Xue et al, 2011).…”
Section: Methodsmentioning
confidence: 99%
“…Where "no" means that respondents would use/used an internal channel. PLS SEM is found to handle these types of variables under certain conditions well (Bodoff & Ho, 2016). comparably well with an R 2 = 0.131 (adj.…”
Section: Robustness Checksmentioning
confidence: 69%
“…Where goodness-of-fit is satisfactory, the model shows that there are interrelationships among variables, but where this is inadequate, then the interrelationships among the variables are rejected [58]. At least three observed variables/indicators are recommended and a common practice whereas, problem exists with two or one observed variable as the measurement error cannot be modelled [59]. If models use only two observed variables per latent variable, they are more likely to fail, and therefore error estimates might be unreliable.…”
Section: Discussionmentioning
confidence: 99%