期刊
DECISION SCIENCES
卷 52, 期 2, 页码 362-392出版社
WILEY
DOI: 10.1111/deci.12445
关键词
Cross-Validation; Explanation; Partial Least Squares; Prediction; Structural Equation Modeling
类别
This study introduces a new method, cross-validated predictive ability test (CVPAT), for evaluating the predictive power of theoretical models and demonstrates its utility in model comparison.
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.
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