Prelude #1
Recent advances in predictive model assessment
Check the flyer for details on the event's contents and registration details:
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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 this aim, researchers have proposed new methods that facilitate testing a model’s 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).
TIME & PLACE
Wednesday, October 28 2020, 3.00pm to 5.30pm CET (Berlin time zone)
We run this event via Zoom
REGISTRATION
Please register before October 26, 2020. Please send an email with the subject "Prelude #1 Registration" (it is required that you use this subject to automatically process your registration) with your name, affiliation (if any) and country to [email protected].
Instructors
Christian M. Ringle
Hamburg University of Technology (TUHH), Germany and
University of Waikato, New Zealand
Marko Sarstedt
Otto-von-Guericke University Magdeburg, Germany and
Monash University Malaysia, Malaysia
https://www.marketing.ovgu.de/marketing/en/Team/Head+of+the+Chair-p-966.html
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.
SPONSORS
Cooperation partner
