|Title||What is Predictive about Partial Least Squares?|
|Publication Type||Conference Proceedings|
|Year of Publication||2010|
|Authors||Shmueli, G., and O. Koppius|
|Conference Name||Sixth Symposium on Statistical Challenges in eCommerce Research (SCECR)|
|Conference Start Date||05/06/2010|
|Conference Location||University of Texas at Austin, McCombs School of Business, TX|
Partial least squares (PLS) estimation of path models has become very popular in IS research, as an alternative to covariance-based methods. PLS path modeling is often referred to as being useful for “predictive” applications. In this work, we investigate the predictive aspects of PLS path modeling and its relation to predictive analytics and predictive assessment. In particular, we compare it to neural networks, which share several similarities. We conclude that PLS path modeling (the dominant form of usage in IS) is primarily causal-explanatory in nature.