Findings: Compared to other fields, our results show that several reporting practices are clearly above standard but still leave room for improvement, particularly regarding the consideration of state-of-the art metrics for measurement and structural model assessment. Tying in with prior studies in the field, our review covers reasons for using PLS-SEM, data characteristics, model characteristics, the evaluation of the measurement models, the evaluation of the structural model, reporting, and use of advanced analyses. Methodology: We reviewed all PLS-SEM studies published in six SSCI-indexed hospitality management journals between 20. As PLS-SEM in hospitality research is still in an early stage of development, critically examining its use holds considerable promise in order to counteract misapplications which otherwise might reinforce over time. We systematically examine how PLS-SEM has been applied in major hospitality research journals with the aim of providing important guidance and, if necessary, opportunities for realignment in future applications. While for many researchers, SEM is equivalent to carrying out covariance-based SEM, recent research advocates the use of partial least squares structural equation modeling (PLS-SEM) as an attractive alternative. STRUCTURED ABSTRACT Purpose: Structural equation modeling (SEM) depicts one of the most salient research methods across a variety of disciplines, including hospitality management.

The ten times rule