Partial Least of Squares Path Modeling (PLS-PM) has become a highly utilized statistical tool for business research in recent years. Its flexibility, with no distribution assumptions and its capacity of working with small sample size are often cited as the major characteristics that draw the attention of researchers. Its predictive nature is often cited as one of its more distinctive characteristics, despite the fact that most researchers utilize it only for explanatory purposes. The lack of a formalized algorithm for prediction using PLS-PM models has contributed to the slow development of the technique as a predictive method. In this dissertation we present a suggested algorithm to generate predictions using PLS-PM models, we provide a software implementation as well as a benchmark comparison of its predictive validity against one of the most traditional predictive tools, linear regression. It is then the aim of this dissertation to encourage further research on the subject of PLS-PM as a predictive tool combined with its already known explanatory capabilities, filling the gap in the explanatory-predictive gamut with a reliable method to perform theory informed predictions.

Partial Least of Squares Path Modeling (PLS-PM) has become a highly utilized statistical tool for business research in recent years. Its flexibility, with no distribution assumptions and its capacity of working with small sample size are often cited as the major characteristics that draw the attention of researchers. Its predictive nature is often cited as one of its more distinctive characteristics, despite the fact that most researchers utilize it only for explanatory purposes. The lack of a formalized algorithm for prediction using PLS-PM models has contributed to the slow development of the technique as a predictive method. In this dissertation we present a suggested algorithm to generate predictions using PLS-PM models, we provide a software implementation as well as a benchmark comparison of its predictive validity against one of the most traditional predictive tools, linear regression. It is then the aim of this dissertation to encourage further research on the subject of PLS-PM as a predictive tool combined with its already known explanatory capabilities, filling the gap in the explanatory-predictive gamut with a reliable method to perform theory informed predictions.

CONTENT

ACKNOWLEDEGMENT i
INDEX OF FIGURES iv
INDEX OF TABLES iv
I. INTRODUCTION AND MOTIVATION 1
II. LITERATURE REVIEW 3
1. EXPLANATORY VS PREDICTIVE MODELING 3
2. PARTIAL LEAST SQUARES PATH MODELING (PLS-PM) 5
2.1. STRUCTURAL EQUATION MODELING AND THE CB-SEM METHOD 5
2.2. THE PLS PATH MODEL 6
2.3. THE PATH MODEL THEORY 9
2.4. THE ALGORITHM 10
2.5. PLS-PM UTILIZATION 13
2.6. SOFTWARE IMPLEMENTATION 14
3. PREDICTING WITH PLS-PM 16
3.1. WHAT DO WE PREDICT WITH PLS-PM 17
3.2. PREDICTIVE PLS-PM MODELS 17
III. PROPOSED METHODOLOGY PROCEDURES 19
1. THE PREDICTION ALGORITHM 19
2. EVALUATING PREDICTIVE POWER 21
2.1. THE CROSS-VALIDATION TECHNIQUE 22
2.2. PREDICTIVE POWER MEASURES 23
3 SIMULATING DATA WITH R PACKAGE SIMSEM 23
4 BENCHMARKING OUR PREDICTIONS 24
IV. EVALUATING PREDICTABILITY 25
1. ESTIMATION AND PREDICTION TOOLS 25
2. TEST MODEL 26
2.1. SAMPLE DATA 27
2.2. CROSS-VALIDATION SETUP 27
3. LINEAR MODEL BENCHMARK 27
4. RESULTS 28
4.1. ACCURACY OF PREDICTIONS 28
4.2. PLS VS LM AGREABILITY 30
4.3. PREDICTION ERRORS COMPARISON 31
5. PREDICTIVE POWER AND EXPLAINED THEORY 33
5.1. REINFORCED THEORY AND APPLICABLE MODELS 34
5.2. PREDICTING FROM DIFFERENT PATHS 34
V. CONCLUSIONS AND FUTURE DIRECTIONS 36
REFERENCES v
APPENDIX vii
APPENDIX A: ESTIMATION FUNCTION IN R CODE vii
APPENDIX B: PREDICTION FUNCTION IN R CODE x

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