This chapter presents an overview of the partial least squares (PLS) approach for evaluating structural equation models. The paper aims to develop a guide for evaluating these models, considering the specific requirements of the PLS approach. PLS is advantageous because it requires fewer assumptions compared to covariance structure analysis, yet still provides consistent estimation results, making it a valuable tool for testing theories. Another strength of PLS is its ability to handle both formative and reflective indicators within a single model, making it suitable for exploratory analysis and contributing to theory development. However, there is limited knowledge on how to evaluate PLS models. To address this, the authors developed a detailed guideline for assessing reflective and formative measurement models as well as the structural model. They also applied these evaluation criteria to an empirical model explaining repeat purchasing behavior. The paper highlights the challenges of using covariance structure analysis, which requires strict assumptions such as multivariate normality and a minimum sample size. It also notes that many studies incorrectly specify formative measurement models as reflective ones. The PLS approach offers an alternative, as it can handle both types of indicators. Despite this, few authors have used PLS to analyze formative constructs, often applying the same evaluation criteria as for reflective models or stating that existing criteria are not suitable without providing alternatives. The paper aims to provide comprehensive guidelines for evaluating structural equation models using PLS, taking into account current methodological discussions.This chapter presents an overview of the partial least squares (PLS) approach for evaluating structural equation models. The paper aims to develop a guide for evaluating these models, considering the specific requirements of the PLS approach. PLS is advantageous because it requires fewer assumptions compared to covariance structure analysis, yet still provides consistent estimation results, making it a valuable tool for testing theories. Another strength of PLS is its ability to handle both formative and reflective indicators within a single model, making it suitable for exploratory analysis and contributing to theory development. However, there is limited knowledge on how to evaluate PLS models. To address this, the authors developed a detailed guideline for assessing reflective and formative measurement models as well as the structural model. They also applied these evaluation criteria to an empirical model explaining repeat purchasing behavior. The paper highlights the challenges of using covariance structure analysis, which requires strict assumptions such as multivariate normality and a minimum sample size. It also notes that many studies incorrectly specify formative measurement models as reflective ones. The PLS approach offers an alternative, as it can handle both types of indicators. Despite this, few authors have used PLS to analyze formative constructs, often applying the same evaluation criteria as for reflective models or stating that existing criteria are not suitable without providing alternatives. The paper aims to provide comprehensive guidelines for evaluating structural equation models using PLS, taking into account current methodological discussions.