Partial least squares path modeling
By Jörg Henseler. 2017
474 citations recorded by the source corpus.
Subfields: Organizational Behavior and Human Resource Management
View Paper: https://www.semanticscholar.org/paper/047f61b57aba31e95aa3180e6c1e9eb668618f43
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In Organizational Behavior and Human Resource Management
- Marko Sarstedt
- Christian M. Ringle
- Jörg Henseler
- Joseph F. Hair
- Florian Schuberth
- Ana Castillo López
- Jose Benitez
- Edward E. Rigdon
Citations in the corpus
- Partial least squares structural equation modeling in HRM research (2020, unknown): “…volumes (e.g. Abdi, Chin, Esposito Vinzi, Russolillo, & Trinchera, 2016; Avkiran & Ringle, 2018; Esposito Vinzi, Chin, Henseler, & Wang, 2010; Latan & Noonan, 2017), and textbooks on the method (e.g. Garson, 2016; Hair, Hult, Ringle, & Sarstedt, 2017; Hair, Sarstedt, Ringle, & Gudergan,…”
- When to use and how to report the results of PLS-SEM (2019, background): “In fact, PLS-SEM offers substantial potential for analyzing large data sets, including secondary data, which often does not include comprehensive substantiation on the grounds of measurement theory (Rigdon, 2013).”
- How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research (2020, background): “See Table 1 in Henseler (2017) for a detailed explanation of the different types of operationalization.”
- On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations (2017, methodology): “…is particularly beneficial in big data applications, which typically focus on prediction, rely on complex models with little theoretical substantiation (Stieglitz, Dang-Xuan, Bruns, & Neuberger, 2014), and often lack a comprehensive support on the grounds of measurement theory (Rigdon, 2013).”
- Gain more insight from your PLS-SEM results: The importance-performance map analysis (2016, methodology): “Partial least squares structural equation modeling (PLS-SEM; Chin, 1998; Garson, 2014; Hair, Hult, Ringle, & Sarstedt, 2016; Lohmöller, 1989; Rigdon, 2013; Tenenhaus, Esposito Vinzi, Chatelin, & Lauro, 2005; Wold, 1982) is a variance-based method to estimate path models with latent variables.”