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When checking the PLS-SEM result, one must make sure that the algorithm did not stop because the maximum number of iterations was reached but due to the stop criterion. This number should be sufficiently large (e.g., 300 iterations). This parameter represents the maximum number of iterations that will be used for calculating the PLS results. Moreover, when the path model includes higher-order constructs (often called second-order models), researchers should usually not use the centroid weighting scheme. This weighting scheme provides the highest R² value for endogenous latent variables and is generally applicable for all kinds of PLS path model specifications and estimations. While the results differ little for the alternative weighting schemes, path weighting is the recommended approach.
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Stage 1: Iterative estimation of latent variable scores consists of a 4-steps iterative procedure, which is repeated until convergence has been obtained (or the maximum number of iterations has been reached): The basic PLS algorithm, as suggested by Lohmöller (1989) and as implemented in SmartPLS, includes the following three stages: The weight vectors obtained at convergence satisfy fixed point equations (see Dijkstra, 2010, for a general analysis of such equations and ensuing convergence issues). The PLS path modeling method was developed by Wold (1992) and the PLS algorithm is essentially a sequence of regressions in terms of weight vectors (Henseler et al., 2009). The weight vectors obtained at convergence satisfy fixed point equations. In essence, the PLS-SEM algorithm is a sequence of regressions in terms of weight vectors. The partial least squares (PLS) path modeling method, also called PLS structural equation modeling (PLS-SEM), was developed by Wold (1982) and further improved by Lohmöller (1989).