This paper provides updated guidelines for using partial least squares (PLS) path modeling in new technology research. PLS is a variance-based structural equation modeling technique that is widely used in business and social sciences. It is particularly useful for modeling both factors and composites, making it a powerful tool for new technology research. Recent developments in PLS have led to significant changes in its understanding and application. The paper discusses these changes and offers a fresh perspective on PLS path modeling. PLS path modeling is the preferred method when a structural equation model contains both factors and composites. It allows for the use of confirmatory composite analysis and the heterotrait-monotrait ratio of correlations (HTMT) as new criteria for discriminant validity. The paper provides updated guidelines on how to use PLS, report, and interpret its results. The paper outlines the nature of PLS path modeling, which is based on alternating least squares algorithms that emulate and extend principal component analysis and canonical correlation analysis. PLS path models are defined by two sets of linear equations: the measurement model and the structural model. The measurement model specifies the relationships between constructs and their observed indicators, while the structural model specifies the relationships between constructs. The paper discusses the specification of PLS path models, including the nature of measurement models (factor vs. composite), model identification, sign indeterminacy, special treatments for categorical variables, and sample size determination. It also explains how to assess and report PLS results, including new tests of model fit, the SRMR as an approximate measure of model fit, the new reliability coefficient $ \rho_{A} $, and the HTMT. The paper also outlines ways to extend PLS analyses and contrasts the understanding of PLS with the traditional view. The paper concludes that PLS path modeling is a powerful technique for new technology research and information systems research. It is particularly useful for modeling strong concepts such as innovations, technologies, systems, processes, strategies, and management instruments. The paper emphasizes the importance of using updated guidelines for PLS path modeling to ensure accurate and meaningful results.This paper provides updated guidelines for using partial least squares (PLS) path modeling in new technology research. PLS is a variance-based structural equation modeling technique that is widely used in business and social sciences. It is particularly useful for modeling both factors and composites, making it a powerful tool for new technology research. Recent developments in PLS have led to significant changes in its understanding and application. The paper discusses these changes and offers a fresh perspective on PLS path modeling. PLS path modeling is the preferred method when a structural equation model contains both factors and composites. It allows for the use of confirmatory composite analysis and the heterotrait-monotrait ratio of correlations (HTMT) as new criteria for discriminant validity. The paper provides updated guidelines on how to use PLS, report, and interpret its results. The paper outlines the nature of PLS path modeling, which is based on alternating least squares algorithms that emulate and extend principal component analysis and canonical correlation analysis. PLS path models are defined by two sets of linear equations: the measurement model and the structural model. The measurement model specifies the relationships between constructs and their observed indicators, while the structural model specifies the relationships between constructs. The paper discusses the specification of PLS path models, including the nature of measurement models (factor vs. composite), model identification, sign indeterminacy, special treatments for categorical variables, and sample size determination. It also explains how to assess and report PLS results, including new tests of model fit, the SRMR as an approximate measure of model fit, the new reliability coefficient $ \rho_{A} $, and the HTMT. The paper also outlines ways to extend PLS analyses and contrasts the understanding of PLS with the traditional view. The paper concludes that PLS path modeling is a powerful technique for new technology research and information systems research. It is particularly useful for modeling strong concepts such as innovations, technologies, systems, processes, strategies, and management instruments. The paper emphasizes the importance of using updated guidelines for PLS path modeling to ensure accurate and meaningful results.