Component-based Structural Equation Modelling

By Michel Tenenhaus. 2008

306 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/0722d13b1f149d7a7673dac65624fb44aa0a62e0

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In Computer Networks and Communications

In Management Science and Operations Research

In Organizational Behavior and Human Resource Management

Citations in the corpus

PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement (2010, unknown): “However, very promising researches on this topic are on going and interesting results are expected soon (Tenenhaus 2008b; Tenenhaus and Tenenhaus 2009).”
Using PLS path modeling in new technology research: updated guidelines (2016, methodology): “The composites serve as proxies for the scientific concept under investigation (Ketterlinus et al., 1989; Rigdon, 2012; Maraun and Halpin, 2008; Tenenhaus, 2008)[1].”
On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations (2017, methodology): “Hence, while CB-SEM and PLS-SEM may draw on the same theoretical model, the statistical models assumed by the methods differ fundamentally due to the methods’ differing assumptions, requirements, and imposed constraints (McDonald, 1996; Sarstedt et al., 2016; Tenenhaus, 2008).”
Generalized Structured Component Analysis with Latent Interactions (2010, methodology): “Generalized structured component analysis (GSCA) (Hwang & Takane, 2004) was proposed for component-based structural equation modeling (SEM) where latent variables are defined as weighted composites or components of observed variables ( Tenenhaus, 2008 ).”
Why generalized structured component analysis is not universally preferable to structural equation modeling (2012, methodology): “…studies that have applied or explored GSCA or introduced extensions of GSCA (up to August 2011, Hwang 2007, 2009; Hwang et al. 2007a, 2010a, 2010b; Hwang and Park 2009; Hwang and Takane 2004; Hwang et al. 2007b; Tenenhaus 2008) and renders their empirical findings with regard to GSCA invalid.”
Regularized Generalized Canonical Correlation Analysis (2011, methodology): “in Hanafi & Kiers (2006) and Tenenhaus & Hanafi (2010). In this section, we only consider methods related to optimization problem (19) with all c 1 for jk j k = ≠ .”
Cutoff criteria for overall model fit indexes in generalized structured component analysis (2020, methodology): “GSCA is therefore regarded as a full information method utilizing all the information available in the entire system of equations (Tenenhaus 2008).”
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods (2017, methodology): “Model estimation using sum scores regression (sum scores) is similar to PLS, but differs in that the sum scores approach assumes equal (typically unit) indicator weights, whereas weightings in PLS represent the partialized effect of the indicators on their corresponding construct, and thus control t”
Regularized Generalized Structured Component Analysis (2009, methodology): “Generalized structured component analysis (GSCA) (Hwang & Takane, 2004) was proposed as a component-based approach to structural equation modeling (SEM), in which latent variables are defined as components or weighted composites of observed variables (Chin, 1998; Tenenhaus, 2008; Velicer & Jackson”
The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling (2017, methodology): “The latter involves different techniques such as regression on summed scale (Tenenhaus, 2008), generalized structured component analysis based structural equation modeling (GSCA-SEM) (Henseler, 2012; Hwang & Takane, 2004), and partial least square path modeling (PLS-PM) (Wold, 1982; Henseler,…”
PLS-SEM: Indeed a Silver Bullet (2011, background): “———, Laura Trinchera, Silvia Squillacciotti, and Michel Tenenhaus (2008), “REBUS-PLS: A Response-Based Procedure for Detecting Unit Segments in PLS Path Modelling,” Applied Stochastic Models in Business and Industry, 24 (5), 439–458.”