PLS-2022 Prelude #1: Recent advances in predictive model assessment скачать в хорошем качестве

PLS-2022 Prelude #1: Recent advances in predictive model assessment

Presenters Christian M. Ringle Hamburg University of Technology (TUHH), Germany and University of Waikato, New Zealand, https://www.tuhh.de/hrmo/team/prof-dr... Marko Sarstedt Otto-von-Guericke University Magdeburg, Germany and Monash University Malaysia, Malaysia, https://www.marketing.ovgu.de/marketi... Contents Recent research in partial least squares structural equation modeling (PLS-SEM) underlines the method’s efficacy for causal-predictive analyses (Hair et al., 2019), which aim at testing the predictive power of a model, grounded in well-developed theory (e.g., Chin et al. 2020). Catering to this aim, researchers have proposed new methods that facilitate testing a model and comparing different models in terms of their predictive power. This workshop presents recent advances in this field by discussing PLSpredict, a holdout sample-based procedure, which executes k-fold cross-validation in PLS-SEM (Shmueli et al. 2016; Shmueli et al., 2019), the cross-validated predictive ability test (CVPAT; Liengaard et al. 2020), and model selection criteria (Danks et al. 2020; Sharma et al. 2020). References Chin, W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J., & Cham Tat, H. (2020). Demystifying the Role of Causal-predictive Modeling Using Partial Least Squares Structural Equation Modeling in Information Systems Research. Industrial Management & Data Systems, advance online publication. Danks, N. P., Sharma, P. N., & Sarstedt, M. (2020). Model Selection Uncertainty and Multimodel Inference in Partial Least Squares Structural Equation Modeling (PLS-SEM). Journal of Business Research, 113, 13-24. Liengaard, B., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2020). Prediction: Coveted, Yet Forsaken? Introducing a Cross-validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, Advance online publication. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to Use and How to Report the Results of PLS-SEM. European Business Review, 31(1), 2-24. Sharma, P. N., Shmueli, G., Sarstedt, M., Danks, N., & Ray, S. (2020). Prediction-oriented Model Selection in Partial Least Squares Path Modeling. Decision Sciences, advance online publication. Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The Elephant in the Room: Predictive Performance of PLS Models. Journal of Business Research, 69(19), 4552-4564. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict. European Journal of Marketing, 53(11), 2322-2347.