To read the full version of this content please select one of the options below:

Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research

Wynne Chin (Department of Decision and Information Sciences, C.T. Bauer College of Business, University of Houston, Houston, Texas, USA)
Jun-Hwa Cheah (School of Business and Economics, Universiti Putra Malaysia, Serdang, Malaysia)
Yide Liu (Macau University of Science and Technology, Taipa, China)
Hiram Ting (Faculty of Hospitality and Tourism Management, UCSI University, Kuala Lumpur, Malaysia)
Xin-Jean Lim (School of Business and Economics, Universiti Putra Malaysia, Serdang, Malaysia)
Tat Huei Cham (Faculty of Accountancy and Management, Universiti Tunku Abdul Rahman, Kajang, Malaysia)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 6 August 2020

Issue publication date: 2 December 2020

Abstract

Purpose

Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.

Design/methodology/approach

A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.

Findings

The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.

Originality/value

This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

Keywords

Acknowledgements

The authors would like to thank Marko Sarstedt and Benjamin Dybro Liengaard for guiding us the new PLS prediction techniques (i.e., information theoretic model selection criteria and cross-validated predictive ability test) in this manuscript.

Citation

Chin, W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J. and Cham, T.H. (2020), "Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research", Industrial Management & Data Systems, Vol. 120 No. 12, pp. 2161-2209. https://doi.org/10.1108/IMDS-10-2019-0529

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited