Abstract
ABSTRACTManagement 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.
Highlights
Management and social science disciplines have historically placed substantial emphasis on theory and understanding, where prediction devoid of a causal explanation is considered suspect and attributed to chance correlation (Douglas, 2009; Tsang, 2009)
We propose a stepwise predictive model comparison procedure to guide researchers, and demonstrate cross-validated predictive ability test (CVPAT)’s practical utility using the well-known American Customer Satisfaction Index (ACSI) model. [Submitted: May 3, 2019
We investigated CVPAT’s ability to correctly reject the null hypothesis that the population model (PM)
Summary
Management and social science disciplines have historically placed substantial emphasis on theory and understanding, where prediction devoid of a causal explanation is considered suspect and attributed to chance correlation (Douglas, 2009; Tsang, 2009). Deprived of the ability to predict, a causal explanation becomes unverifiable and uncontradictable, and so loses its practical relevance (Shmueli, 2010). In his seminal treatise on the philosophy of science, Conjectures and Refutations, Popper (1962) posited that prediction is the primary criterion for evaluating falsifiability and that all explanatory theories must “rise and fall based on their objective predictions” The explanation-oriented models typically used in the management and social science disciplines often ignore predictive power assessments using established methods such as cross-validation (Shmueli & Koppius, 2011; Yarkoni & Westfall, 2017). Researchers frequently deal with latent constructs such as individuals’
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