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

TY - JOUR

T1 - Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms

AU - Richter, Nicole Franziska

AU - Tudoran, Ana Alina

PY - 2024/2

Y1 - 2024/2

N2 - 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 research models, enhance the understanding of relationships, assist in identifying new relationships and therewith contribute to theorizing. We demonstrate the advantages and challenges of triangulating the two methods on an illustrative example along a four-step-routine: (1) Develop a PLS-SEM on a baseline conceptual model and use its standards to assess measurement model quality and generate latent variables scores. (2) Apply specific ML algorithms on the extracted data to validate relationships and identify new (linear) relationships that may go beyond the initial hypotheses; similarly, assess model advancements in the form of nonlinearities and interaction effects. (3) Evaluate thetheoretical plausibility of alternative models. (4) Integrate alternative models in PLS-SEM and compare these with the baseline model using a recently proposed prediction-oriented test procedure in PLS-SEM.

AB - 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 research models, enhance the understanding of relationships, assist in identifying new relationships and therewith contribute to theorizing. We demonstrate the advantages and challenges of triangulating the two methods on an illustrative example along a four-step-routine: (1) Develop a PLS-SEM on a baseline conceptual model and use its standards to assess measurement model quality and generate latent variables scores. (2) Apply specific ML algorithms on the extracted data to validate relationships and identify new (linear) relationships that may go beyond the initial hypotheses; similarly, assess model advancements in the form of nonlinearities and interaction effects. (3) Evaluate thetheoretical plausibility of alternative models. (4) Integrate alternative models in PLS-SEM and compare these with the baseline model using a recently proposed prediction-oriented test procedure in PLS-SEM.

KW - Machine learning (ML)

KW - Method triangulation

KW - Partial least squares-structural equation modeling (PLS-SEM)

KW - Prediction

KW - Unified theory of acceptance and use of technology (UTAUT)

UR - http://www.scopus.com/inward/record.url?scp=85181142471&partnerID=8YFLogxK

U2 - 10.1016/j.jbusres.2023.114453

DO - 10.1016/j.jbusres.2023.114453

M3 - Journal article

SN - 0148-2963

VL - 173

JO - Journal of Business Research

JF - Journal of Business Research

IS - 114453

M1 - 114453

ER -