A review of recent approaches for capturing heterogeneity in partial least squares path modelling

By Marko Sarstedt. 2008

180 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/43778d4a9425306b92f0446d7b9d4dc73561255b

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Citations in the corpus

Tourism Destination Competitiveness: Second Thoughts on the World Economic Forum Reports (2011, methodology): “Recent comparison studies of heterogeneity-capturing PLS methods by Esposito Vinzi et al (2007) and Sarstedt (2008) demonstrate that the FIMIX-PLS routine as implemented in SmartPLS excels five rivalling approaches.”
Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies (2010, methodology): “In addition to PLS-GAS [33,35] and REBUS-PLS [12], FIMIX-PLS can currently be regarded as a key approach with which to capture heterogeneity in PLS-PM, despite some limitations [42].”
Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers (2014, methodology): “To start with, we briefly explain the fundamentals of SEM in accordance with Hair, Hult, Ringle, and Sarstedt (2014). The SEM”
Testing measurement invariance of composites using partial least squares (2016, background): “Various techniques allow researchers to uncover unobserved heterogeneity (Sarstedt, 2008).”
Intrinsic motivation of Chinese learning in predicting online learning self-efficacy and flow experience relevant to students’ learning progress (2017, background): “contrast, PLS is based on a small size to explain complex relationships (Sarstedt, 2008).”
PLS-SEM: Looking Back and Moving Forward (2014, methodology): “Dijkstra (2014), PLS’ Janus Face, and Sarstedt et al. (2014a), On the Emancipation of PLS-SEM.”
Structural model robustness checks in PLS-SEM (2020, background): “Latent class techniques for identifying and treating unobserved heterogeneity have a long-standing tradition in PLS-SEM (Sarstedt, 2008).”
PLS-SEM: Indeed a Silver Bullet (2011, methodology): “Among these, FIMIX-PLS (Hahn et al. 2002; Sarstedt and Ringle 2010) is to date regarded as the primary approach and has become mandatory for evaluating PLS path modeling results (Sarstedt 2008).”
Finite Mixture Partial Least Squares Analysis: Methodology and Numerical Examples (2010, methodology): “Sarstedt (2008) reviews existing segmentation techniques for PLS path modeling and concludes that FIMIX-PLS can currently be viewed as the most comprehensive and commonly used approach to capture heterogeneity in PLS path modeling.”
Online impulse buying of tourism products: The role of web site personality, utilitarian and hedonic web browsing (2016, unknown): “Furthermore, PLS does not required assumptions such as normality of data distributions and sample size (Vinzi et al., 2010), and it is an advantage when the primary concern of the analysis is the prediction originated or prediction accuracy (Sarstedt, 2008; Hair et al., 2011; Rezaei, 2015).”
Multigroup Analysis in Partial Least Squares (PLS) Path Modeling: Alternative Methods and Empirical Results (2011, methodology): “Finite mixture PLS (FIMIX-PLS; Sarstedt & Ringle, 2010; Hahn, Johnson, Herrmann, & Huber, 2002; Sarstedt, Becker, Ringle, & Schwaiger, 2011) is currently regarded the primary approach of all these segmentation techniques, and has become mandatory for evaluating PLS path modeling results (Sarstedt, 2”