The Potential of Partial Least Squares Structural Equation Modeling (PLS-SEM) with a Formative Approach in Accounting Research

Accounting research faces methodological challenges when investigating complex phenomena, often modeled by interrelated latent variables. In this context, Partial Least Squares Structural Equation Modeling (PLS-SEM) emerges as a robust alternative to multiple regression, particularly for modeling formative constructs. Unlike traditional methods, PLS-SEM allows for the simultaneous estimation of direct, indirect (mediated), and moderated relationships, making it especially useful for analyzing documentary and secondary data, which are commonly used in financial accounting. Therefore, this editorial explores the potential of the PLS-SEM formative approach in accounting research, highlighting applications in corporate governance, audit quality, organizational sustainability, and operational efficiency. The use of second-order constructs is discussed as a solution for capturing the complexity of these phenomena. Additionally, the relevance of PLS-SEM is underscored in the analysis of mediating and moderating effects, which are challenging to assess through traditional regression methods. By enabling more comprehensive and detailed analyses, PLS-SEM provides statistically robust models and greater precision in interpreting results. This article contributes to the literature by demonstrating the technique’s applicability and encouraging its adoption in accounting research.