Bootstrap Cross-Validation Indices for PLS Path Model Assessment

By Wynne W. Chin. 2010

358 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c70ba7976b1d55b157817194725956d287b36c48

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In Industrial and Manufacturing Engineering

In Management Science and Operations Research

Citations in the corpus

Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling (2017, background): “Whereas the older literature on variance-based SEM tends to equate formative measurement with composite measurement (e.g., see Chin 1998; Hwang and Takane 2004), it is only recently that scholars started recommending the multiple indicators, multiple causes (MIMIC) model specification for…”
Using PLS path modeling in new technology research: updated guidelines (2016, background): “Criteria for the predictive capability of structural models have been proposed (cf. Chin, 2010), but still need to disseminate.”
How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents (2020, methodology): “The statistical significance of the path coefficients was estimated based on the bootstrapping technique as recommended by (Chin, 2010).”
Evaluation of Structural Equation Models Using the Partial Least Squares (PLS) Approach (2010, background): “The indicators’ weights and their bootstrap t-statistics (cf. Chin 2010 ) are presented in Table 29.2.”
How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM (2019, methodology): “Chin, W.W. , 2010b.”
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods (2017, methodology): “Addressing corresponding calls in prior research (Chin 2010; Hwang et al. 2010; Marcoulides and Chin 2013; Marcoulides et al. 2012), this study is the first to provide a systematic assessment of and comparison between PLS, GSCA, and the sum scores approach based on composite model data that is…”