No abstract

Purpose Indirect or mediated effects constitute a type of relationship between constructs that often occurs in partial least squares (PLS) path modeling. Over the past few years, the methods for testing mediation have become more sophisticated. However, many researchers continue to use outdated methods to test mediating effects in PLS, which can lead to erroneous results. One reason for the use of outdated methods or even the lack of their use altogether is that no systematic tutorials on PLS exist that draw on the newest statistical findings. The paper aims to discuss these issues. Design/methodology/approach This study illustrates the state-of-the-art use of mediation analysis in the context of PLS-structural equation modeling (SEM). Findings This study facilitates the adoption of modern procedures in PLS-SEM by challenging the conventional approach to mediation analysis and providing more accurate alternatives. In addition, the authors propose a decision tree and classification of mediation effects. Originality/value The recommended approach offers a wide range of testing options (e.g. multiple mediators) that go beyond simple mediation analysis alternatives, helping researchers discuss their studies in a more accurate way.

Indirect or mediated effects constitute a type of relationship between constructs that often occurs in partial least squares structural equation models (PLS-SEM). Over the past few years, the methods for testing mediation have become more sophisticated. However, many researchers continue to use outdated methods to test mediation effects in PLS-SEM, which can lead to erroneous results. One reason for the use of outdated methods or even ignoring it is that no systematic tutorials on PLS-SEM exist that draw on the newest statistical findings. This study illustrates the state-of-the-art use of mediation analysis in the context of PLS-SEM. It facilitates the adoption of modern procedures in PLS-SEM by challenging the conventional approach to mediation analysis and providing alternatives that are more accurate. In addition, we propose a decision tree and classification of mediation effects. Our recommended approach offers a wide range of testing options that go beyond simple mediation analysis alternatives, helping researchers discuss their studies in a more accurate way.Acknowledgments The authors would like to thank Joseph F. Hair, Christian M. Ringle, and Marko Sarstedt for their fruitful comments and ideas regarding an earlier version of this manuscript.

Commonly used discrete choice model analyses (e.g., probit, logit and multinomial logit models) draw on the estimation of importance weights that apply to different attribute levels. But directly estimating the importance weights of the attribute as a whole, rather than of distinct attribute levels, is challenging. This article substantiates the usefulness of partial least squares structural equation modeling (PLS-SEM) for the analysis of stated preference data generated through choice experiments in discrete choice modeling. This ability of PLS-SEM to directly estimate the importance weights for attributes as a whole, rather than for the & Christian M. Ringle

In management accounting research, the capabilities of Partial Least Squares Structural Equation Modelling (PLS-SEM) have only partially been utilized. These yet unexploited capabilities of PLS-SEM are a useful tool in the often explorative state of research in management accounting. After reviewing eleven top-ranked management accounting journals through the end of 2013, 37 articles in which PLS-SEM is used are identified. These articles are analysed based on multiple relevant criteria to determine the progress in this research area, including the reasons for using PLS-SEM, the characteristics of the data and the models, and model evaluation and reporting. A special focus is placed on the degree of importance of these analysed criteria for the future development of management accounting research. To ensure continued theoretical development in management accounting, this article also offers recommendations to avoid common pitfalls and provides guidance for the advanced use of PLS-SEM in management accounting research.