Department of Economics and Business Economics

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

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  • Benjamin Dybro Liengaard
  • Pratyush Nidhi Sharma, University of Delaware
  • ,
  • G. Tomas M. Hult, Michigan State University
  • ,
  • Morten Berg Jensen
  • Marko Sarstedt, Monash University Malaysia, Otto von Guericke University Magdeburg
  • ,
  • Joseph F. Hair, University of South Alabama
  • ,
  • Christian M. Ringle, Hamburg University of Technology, University of Waikato

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.

Original languageEnglish
JournalDecision Sciences
Pages (from-to)362-392
Number of pages31
Publication statusPublished - Apr 2021

    Research areas

  • Cross-Validation, Explanation, Partial Least Squares, Prediction, Structural Equation Modeling

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