The elephant in the room: Predictive performance of PLS models

By G. Shmueli, Soumya Ray, Juan Manuel Velasquez Estrada, S. Chatla. 2016

1,534 citations recorded by the source corpus.

Subfields: Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/8b182eb550f18eaf25d7878ed3561c93b50150b7

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In Management Information Systems

In Management Science and Operations Research

Citations in the corpus

Partial least squares structural equation modeling in HRM research (2020, methodology): “procedure helps substantiate the stability and generalizability of the model estimates (Cepeda Carrión, Henseler, Ringle, & Roldán, 2016; Shmueli et al., 2016).”
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods (2017, methodology): “…to the next Jöreskog (1973); Tenenhaus et al. (2005) Estimation objective Explanatory modeling Predictive modeling Rigdon (2012); Shmueli (2010); Shmueli et al. (2016) Treatment of construct measures Treats constructs as common factors–only common variance is used to estimate model…”
Partial least squares path modeling: Quo vadis? (2018, background): “and descriptive research in two important ways (Shmueli et al. 2016).”
How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research (2020, methodology): “A further promising application of PLS-PM in IS research is predictive modeling, which aims to produce accurate forecasts [23].”
Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling (2017, background): “The first advances in this direction have recently been presented by Cepeda Carri on et al. (2016), Evermann and Tate (2016), and Shmueli et al. (2016).”
Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results (2016, background): “…performance contributes to the developing of new theory, developing and evaluating as well as improving construct operationalization, comparing theories, improving existing theory, assessing relevance, and evaluating the predictability of phenomena (Shmueli & Koppius, 2011; Shmueli et al., 2016).”
Multigroup Analysis using SmartPLS: Step-by-Step Guidelines for Business Research (2020, positive): “…al., 2020), necessary condition analysis (Richter et al., 2020), the endogeneity test in PLS (Hult et al., 2018; Sarstedt et al., 2020), PLSpredict (Shmueli et al., 2016, 2019), model selection criteria (Danks et al., 2020; Sharma, 2019a, 2019b), and the cross-validated predictive ability test…”