Finite Iterative Method Based Algorithm To Estimate Latent Variables In Partial Least Squares Path Modelling For Mode A

Partial Least Squares Structural Equation Modeling (PLS-SEM) is a powerful statistical approach that has become a mainstream method in many application areas. It offers flexibility in handling formative and reflective measurement blocks, enabling researchers to model relationships among observed and latent variables. The crucial step in this approach is the PLS-SEM algorithm, which involves computing the scores of latent variables by alternating between inner and outer estimation. The aims of the present paper are twofold. The first contribution shows that the computations used in the outer estimation are inappropriate for reflective blocks. The second contribution involves introducing an alternative algorithm to overcome this rawback by using a new strategy based on considering the true structure of reflective blocks. Numerical studies and empirical simulations are provided to illustrate the advantages of the proposed algorithm compared to the classical one.