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

2020/05/22 by Benjamin D. Liengaard, Benjamin Dybro Liengaard, Pratyush Nidhi Sharma +5 · 11 citations
Business, Management and Accounting · Decision Sciences · Economics, Econometrics and Finance · #Customer Service Quality and Loyalty #Economic and Environmental Valuation #Technology Adoption and User Behaviour

paper · pdf · doi:10.1111/deci.12445

crossref issued 2020/05/22 · crossref published 2020/05/22 · crossref published-online 2020/05/22 · openalex publication_date 2020/05/22 · crossref created 2020/05/22 · crossref published-print 2021/04/01 · crossref deposited 2024/08/06 · openalex created_date 2025/10/10 · crossref indexed 2026/07/30 · openalex updated_date 2026/07/30

Abstract

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.

Citations

• Research Commentary—Too Big to Fail: Large Samples and the p-Value Problem
• The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications
• Rethinking Partial Least Squares Path Modeling: Breaking Chains and Forging Ahead
• Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory
• Rethinking Partial Least Squares Path Modeling: In Praise of Simple Methods
• The perils of policy by p-value: Predicting civil conflicts
• User Acceptance of Information Technology: Toward A Unified View1
• An assessment of the use of partial least squares structural equation modeling in marketing research
• Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
• The ASA Statement on p -Values: Context, Process, and Purpose
• Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
• Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods
• Partial least squares structural equation modeling in HRM research

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• Progress in partial least squares structural equation modeling use in marketing research in the last decade
• Partial Least Squares Structural Equation Modeling
• PLS-SEM’s most wanted guidance
• Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modeling
• Going beyond the untold facts in PLS–SEM and moving forward
• Executing and interpreting applications of PLS-SEM: Updates for family business researchers
• PLS-SEM in information systems: seizing the opportunity and marching ahead full speed to adopt methodological updates
• Consistent Partial Least Squares Structural Equation Modeling Using SmartPLS
• A Multimethod SEM Framework for Analyzing Models with Latent Variables

Related

• Prediction‐Oriented Model Selection in Partial Least Squares Path Modeling
• Predictive model assessment and selection in composite-based modeling using PLS-SEM: extensions and guidelines for using CVPAT
• Predictive model assessment in PLS-SEM: guidelines for using PLSpredict
• Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research
• Out of Sample Predictability in Predictive Regressions with Many Predictor Candidates