Purpose The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness. Design/methodology/approach This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM. Findings Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses. Research limitations/implications Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method. Originality/value In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.

Structural equation modeling, often referred to as SEM, is a well‐established, covariance‐based multivariate method used in Human Resource Development (HRD) quantitative research. In some research contexts, however, the rigorous assumptions associated with covariance‐based SEM (CB‐SEM) limit applications of the method. An emergent complementary SEM approach, partial least squares structural equation modeling (PLS‐SEM), is a variance‐based SEM method that provides valid solutions and overcomes several limitations associated with CB‐SEM. Despite PLS‐SEM's increasing popularity in many social sciences disciplines, the method has yet to gain traction in the field of HRD. An accessible overview of the method, including potential advantages for HRD research and extant methodological advancements, is provided in this article with the goal of encouraging productive dialogue in the field of HRD surrounding the PLS‐SEM approach. We present an emergent analytical tool for quantitative HRD research, offer practical guidelines for researchers to consider when selecting a SEM method, and clarify assessment stages and up‐to‐date evaluation criteria through an illustrative example.

The role of marketing for consumers and businesses is rapidly evolving (Ferrell, Hair, Marshall Tamilia 2015). Indeed, the marketing function is fundamentally changing as a result of the digital transformation, data, data analytics and most recently, personal mobile devices, such as infusion software, which track time as individuals work or play, can search for local businesses or customers while in a coffee shop, send an invoice or quote from a clients office, and much more (Shah et al 2014). Marketing research design and analysis methods are also changing rapidly (Hair, Black, Babin Anderson 2019). These changes are emerging from transformations in management skills (Henke, Levine McInerney 2018; Davenport 2018), technological innovation, particularly in the digital space (Davenport 2018), and continuously evolving customer behavior (needs and expectations) (Wedel Kannan 2016). To continue being relevant, marketing research must remain as dynamic as the markets themselves.