On the use of partial least squares path modeling in accounting research

The benefits of using PLS are explained and the algorithm works to derive estimates for the measurement and structural models and guidelines in the development of research models, analysis of the data, and the interpretation of these results are offered.

Abstract

Partial least squares (PLS) is an approach to structural equation modeling (SEM) that is extensively used in the social sciences to analyze quantitative data. However, PLS has not been as readily adopted in the accounting discipline. A review of the accounting literature found 20 studies in a subset of accounting journals that used PLS as the data analysis tool. PLS allows researchers to analyze the measurement model simultaneously with the structural model and allows researchers to adopt more complex research models with both moderating and mediating relationships. This paper assists accounting researchers that may be interested in adopting PLS as an analysis tool. We explain the benefits of using PLS and compare and contrast this analysis approach with both ordinary least squares regression and covariance-based SEM. We also explain how the PLS algorithm works to derive estimates for the measurement and structural models. To further assist researchers interested in using PLS, we offer guidelines in the development of research models, analysis of the data, and the interpretation of these results with PLS. We apply these guidelines to the accounting studies that have used PLS and offer further recommendations about how researchers could apply PLS in future accounting research.