Journal
JOURNAL OF BUSINESS RESEARCH
Volume 69, Issue 10, Pages 4565-4582Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jbusres.2016.03.050
Keywords
Prediction; Structural equation models; Partial least squares; Simulation study
Categories
Ask authors/readers for more resources
Structural equation models are traditionally used for theory testing. With the increasing importance of predictive analytics, and the ability of structural equation models to maintain theoretical plausibility in the context of predictive modeling, identifying how best to predict from structural equation models is important. Recent calls for a refocusing of partial least squares path modeling (PLSPM) on predictive applications further increase the need to assess and compare the predictive power of different estimation methods for structural equation models. This paper presents two simulation studies that evaluate the performance of different modes and variations of PLSPM and covariance analysis on prediction from structural equation models. Study 1 examines all-reflective models using blindfolding and the Q(2) statistic. Study 2 examines mixed formative-reflective models using out-of-sample cross-validation and the RMSE statistic. Recommendations to guide researchers in the choice of appropriate prediction method are offered. (C) 2016 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available