Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict

European Journal of Marketing, 53(11), 2322-2347, Forthcoming

Posted: 25 Oct 2019

See all articles by Galit Shmueli

Galit Shmueli

Institute of Service Science, National Tsing Hua University, Taiwan

Marko Sarstedt

Otto-von-Guericke-Universität Magdeburg; Otto-von-Guericke-Universität Magdeburg; University of Newcastle (Australia)

Joseph F. Hair

Kennesaw State University

Jacky Cheah

affiliation not provided to SSRN

Hiram Ting

Universiti Malaysia Sarawak (UNIMAS)

Santha Vaithilingam

Monash University Malaysia

Christian M. Ringle

Hamburg University of Technology (TUHH)

Date Written: 2019

Abstract

Purpose - Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for applying PLSpredict and explains the key choices researchers need to make using the procedure.

Design/methodology/approach - The authors discuss the need for prediction-oriented model evaluations in PLS-SEM and conceptually explain and further advance the PLSpredict method. In addition, they illustrate the PLSpredict procedure’s use with a tourism marketing model and provide recommendations on how the results should be interpreted. While the focus of the paper is on the PLSpredict procedure, the overarching aim is to encourage the routine prediction-oriented assessment in PLS-SEM analyses.

Findings - The paper advances PLSpredict and offers guidance on how to use this prediction-oriented model evaluation approach. Researchers should routinely consider the assessment of the predictive power of their PLS path models. PLSpredict is a useful and straightforward approach to evaluate the out-of-sample predictive capabilities of PLS path models that researchers can apply in their studies.

Research limitations/implications - Future research should seek to extend PLSpredict’s capabilities, for example, by developing more benchmarks for comparing PLS-SEM results and empirically contrasting the earliest antecedent and the direct antecedent approaches to predictive power assessment.

Practical implications - This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses.

Originality/value - This research substantiates the use of PLSpredict. It provides marketing researchers and practitioners with the knowledge they need to properly assess, report and interpret PLS-SEM results. Thereby, this research contributes to safeguarding the rigor of marketing studies using PLS-SEM.

Keywords: PLS-SEM, partial least squares, structural equation modeling, PLSpredict, out-of-sample prediction, predictive power

Suggested Citation

Shmueli, Galit and Sarstedt, Marko and Hair, Joseph F. and Cheah, Jacky and Ting, Hiram and Vaithilingam, Santha and Ringle, Christian M., Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict (2019). European Journal of Marketing, 53(11), 2322-2347, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3471205

Galit Shmueli (Contact Author)

Institute of Service Science, National Tsing Hua University, Taiwan ( email )

Hsinchu, 30013
Taiwan

HOME PAGE: http://www.iss.nthu.edu.tw

Marko Sarstedt

Otto-von-Guericke-Universität Magdeburg ( email )

Universitätspl. 2
PSF 4120
Magdeburg, D-39106
Germany

Otto-von-Guericke-Universität Magdeburg ( email )

Universitätspl. 2
PSF 4120
Magdeburg, D-39106
Germany

University of Newcastle (Australia) ( email )

University Drive
Callaghan, NSW 2308
Australia

Joseph F. Hair

Kennesaw State University ( email )

1000 Chastain Rd
Kennesaw, GA 30144
United States

Jacky Cheah

affiliation not provided to SSRN

Hiram Ting

Universiti Malaysia Sarawak (UNIMAS) ( email )

Kota Samarahan, Sarawak 94300
Malaysia

Santha Vaithilingam

Monash University Malaysia ( email )

Level 4, Building 6
Jalan Lagoon Selatan
Selangor Darul Ehsan 46150
Malaysia
+603-55146390 (Phone)
+603-55146192 (Fax)

HOME PAGE: http://www.buseco.monash.edu.my/school-staff/Santha-Vaithilingam-Assoc.-Prof.html

Christian M. Ringle

Hamburg University of Technology (TUHH) ( email )

Am Schwarzenberg-Campus 4
Hamburg, 21073
Germany

HOME PAGE: http://www.tuhh.de/hrmo

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