Although previous studies have offered procedures to perform predictive analytics on PLS path models, there are no guidelines for practitioners on what good or bad predictive characteristics are, what kinds of PLS models might benefit from predictive analysis, and how predictive characteristics of PLS models should be displayed and reported in publications. We seek to rectify this gap in academic practice by systematically analyzing the predictive performance of simulated PLS models with varying measurement and structural properties, and by visually examining their predictive performance.
We used the SimSem package to simulate data for these models. This package was created for the R Statistical Environment in order to perform MonteCarlo simulations of Structural Equation Models. We generated three simulated models: (a) a model with near perfect measurement and structural properties; (b) a model with deteriorated measurement more typical to real world studies; and (c) a model using data following a discrete Likert-type scale typically used in psychometric and management studies.
Our findings show that under excellent conditions, PLS models can indeed generate highly precise case-level predictions. However, we also illustrate there are lower limits of measurement quality below which studies simply cannot offer practical case-level claims. We also found that Likert scales can be accommodated by predictive PLS, though we caution that there is still room for debate on what prediction means in these situations. Finally, we show that all the above situations can be found in empirical studies, and offer ways to interpret these results.

Although previous studies have offered procedures to perform predictive analytics on PLS path models, there are no guidelines for practitioners on what good or bad predictive characteristics are, what kinds of PLS models might benefit from predictive analysis, and how predictive characteristics of PLS models should be displayed and reported in publications. We seek to rectify this gap in academic practice by systematically analyzing the predictive performance of simulated PLS models with varying measurement and structural properties, and by visually examining their predictive performance.
We used the SimSem package to simulate data for these models. This package was created for the R Statistical Environment in order to perform MonteCarlo simulations of Structural Equation Models. We generated three simulated models: (a) a model with near perfect measurement and structural properties; (b) a model with deteriorated measurement more typical to real world studies; and (c) a model using data following a discrete Likert-type scale typically used in psychometric and management studies.
Our findings show that under excellent conditions, PLS models can indeed generate highly precise case-level predictions. However, we also illustrate there are lower limits of measurement quality below which studies simply cannot offer practical case-level claims. We also found that Likert scales can be accommodated by predictive PLS, though we caution that there is still room for debate on what prediction means in these situations. Finally, we show that all the above situations can be found in empirical studies, and offer ways to interpret these results.

TABLE OF CONTENTS
Abstract 3
1 ACKNOWLEDGEMENT 4
2 INTRODUCTION 7
3 LITERATURE REVIEW 9
3.1 Motivation for using PLS-PM 9
3.2 Motivation for using PLS-PM for prediction 10
3.3 Notes on Prediction 11
3.4 PLS-PM use in the literature 12
3.5 Implementation of PLS-PM 12
3.6 Visualization 13
3.7 Measurement Quality 13
3.8 Measurement Scales 15
4 APPLYING PLS-PM PREDICTION 16
4.1 Overview of Algorithm Implementation 16
4.2 Overcoming Implementation Challenges 17
4.2.1 Interval Estimation – Quantiles 17
4.2.2 Bootstrap Considerations 18
4.2.3 Prediction Metric Aggregation 18
4.3 Visualization 19
4.4 Measurement Quality and Prediction Metrics 20
4.4.1 High Measurement Quality 21
4.4.2 Low Measurement Quality 22
4.5 Measurement Scales and Likert Data 23
5 DISCUSSION 25
5.1 Advantages of Data Simulation 25
5.2 Lessons from Implementation 25
5.3 Practical use of Visualizations 26
5.4 Considerations of Measurement Quality 27
5.5 Considerations of using Likert scale 27
6 LIMITATIONS AND FUTURE WORK 29
6.1 Simulation techniques 29
6.2 Holdout vs Cross Validation 29
6.3 Validating the Effectiveness of Visualizations 30
6.4 Likert data and prediction 30
7 CONCLUSION 31
8 REFERENCES 33
9 TABLES 35
10 CODE IMPLEMENTED 36
10.1 predictionInterval 36
10.2 validatePredict 38

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