Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms

We propose a routine for combining partial least squares-structural equation modeling (PLS-SEM) with selected machine learning (ML) algorithms to exploit the two method’s causal-predictive and causal-exploratory capabilities. Triangulating these two methods can improve the predictive accuracy of res...

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Vydané v:	Journal of business research Ročník 173; s. 114453
Hlavní autori:	Richter, Nicole Franziska, Tudoran, Ana Alina
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.02.2024
Predmet:	Machine learning (ML) Method triangulation Partial least squares-structural equation modeling (PLS-SEM) Prediction Unified theory of acceptance and use of technology (UTAUT) Machine learning (ML) Partial least squares-structural equation modeling (PLS-SEM) Method triangulation Prediction Unified theory of acceptance and use of technology (UTAUT)
ISSN:	0148-2963, 1873-7978
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