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.

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