Prediction: Coveted, Yet Forsaken? Introducing a Cross‐Validated Predictive Ability Test in Partial Least Squares Path Modeling

Abstract

Management researchers often develop theories and policies that are forward‐looking. The prospective outlook of predictive modeling, where a model predicts unseen or new data, can complement the retrospective nature of causal‐explanatory modeling that dominates the field. Partial least squares (PLS) path modeling is an excellent tool for building theories that offer both explanation and prediction. A limitation of PLS, however, is the lack of a statistical test to assess whether a proposed or alternative theoretical model offers significantly better out‐of‐sample predictive power than a benchmark or an established model. Such an assessment of predictive power is essential for theory development and validation, and for selecting a model on which to base managerial and policy decisions. We introduce the cross‐validated predictive ability test (CVPAT) to conduct a pairwise comparison of predictive power of competing models, and substantiate its performance via multiple Monte Carlo studies. We propose a stepwise predictive model comparison procedure to guide researchers, and demonstrate CVPAT's practical utility using the well‐known American Customer Satisfaction Index (ACSI) model.

Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses!

“…A potential objection regarding the use of PLS-SEM compared with factor-based SEM could be that the method does not offer model modification indices to readily address potential problems of model misspecification as caused by, for example, omitted variables. However, recent research has introduced procedures for comparing PLS path models in terms of model fit and predictive power (Liengaard et al, 2020; Sharma et al, 2019, 2020). While these procedures do not provide a stand-alone assessment of a model, they allow contrasting different model configurations that vary, for example, the position of a moderator in a conditional process model.…”

Cutoff criteria for overall model fit indexes in generalized structured component analysis

“…An acceptable model fit does not necessarily guarantee the model's plausibility and this judgement should be made based on substantive theory (Byrne 2001, p. 88). Furthermore, a well-fitting model does not necessarily warrant enough predictive power, which is important to substantiate practical recommendations derived from any model Liengaard et al 2020). 177 .177 .177 .177 .177 .177 .177 .187 .430 .500 .500 .93 .311 .311 .311 .311 .311 .311 .311 .313 .435 .500 .500 .94 .412 .412 .412 .412 .412 .412 .412 .412 .456 .500 .500 .95 .479 .479 .479 .479 .479 .479 .479 .479 .482 86 .198 .198 .198 .198 .198 .193 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.…”

Personal factors, entrepreneurial intention, and entrepreneurial status: A multinational study in three institutional environments

“…Yet, identifying personal (and institutional) determinants that predict entrepreneurial outcomes may be of specific value to governments and their political action plans. Several methodological advancements can assist researchers in identifying the predictive power of determinants (see Liengaard et al 2020;Richter et al 2016c).…”