With applications using SmartPLS (www.smartpls.com)—the primary software used in partial least squares structural equation modeling (PLS-SEM)—this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.

A primer on partial least squares structural equation modeling (PLS-SEM)

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.,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.,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.,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.,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.

https://www.emerald.com/insight/content/doi/10.1108/EBR-11-2018-0203/full/pdf

When to use and how to report the results of PLS-SEM

This second Long Range Planning special issue on PLS-SEMin strategic management research and practice seeks to further progress towards this goal. The journal received 41 articles for its special issue on PLS-SEM, twelve of which completed a thorough review process successfully. Based on the number of high quality manuscripts, a decision was made to split the special issue. In the first Long Range Planning special issue on PLS-SEM in strategic management (Hair et al., 2012a; Robins, 2012), the focus was on methodological developments and their application (Becker et al., 2012; Furrer et al., 2012; Gudergan et al., 2012; Hair et al., 2012a,b,c; Money et al., 2012; Rigdon, 2012). This second special issue provides a forum for topical issues that demonstrate the usefulness of PLS-SEM by piloting applications of this method in the field of strategic management with strong implications for strategic research and practice. As such, the special issue targets two audiences: academics involved in the fields of strategy and management, and practitioners such as consultants. The six articles in this issue are summarized in the following paragraphs.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2233795_code1463265.pdf?abstractid=2233795&mirid=4

Editorial - Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance

Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance

Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method.

Partial Least Squares Structural Equation Modeling

Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM): Eine anwendungsorientierte Einführung

European management research using partial least squares structural equation modeling (PLS-SEM)

Purpose – Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM (CB-SEM) approach is dominant, the authors argue that the field’s dynamic nature and the sometimes early stage of theory development more often require a partial least squares SEM (PLS-SEM) approach. The purpose of this paper is to critically review the application of SEM techniques in the field. Design/methodology/approach – The authors searched six journals with an international business (and marketing) focus (Management International Review, Journal of International Business Studies, Journal of International Management, International Marketing Review, Journal of World Business, International Business Review) from 1990 to 2013. The authors reviewed all articles that apply SEM, analyzed their research objectives and methodology choices, and assessed whether the PLS-SEM papers followed the best practices outlined in the past. ...

/pdf/a-critical-look-at-the-use-of-sem-in-international-business-35lz8u9hlp.pdf

A critical look at the use of SEM in international business research

This research introduces the combined use of partial least squares–structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) that enables researchers to explore and validate hypotheses following a sufficiency logic, as well as hypotheses drawing on a necessity logic. The authors’ objective is to encourage the practice of combining PLS-SEM and NCA as complementary views of causality and data analysis.,The authors present guidelines describing how to combine PLS-SEM and NCA. These relate to the specification of the research objective and the theoretical background, the preparation and evaluation of the data set, running the analyses, the evaluation of measurements, the evaluation of the (structural) model and relationships and the interpretation of findings. In addition, the authors present an empirical illustration in the field of technology acceptance.,The use of PLS-SEM and NCA enables researchers to identify the must-have factors required for an outcome in accordance with the necessity logic. At the same time, this approach shows the should-have factors following the additive sufficiency logic. The combination of both logics enables researchers to support their theoretical considerations and offers new avenues to test theoretical alternatives for established models.,The authors provide insights into the logic, assessment, challenges and benefits of NCA for researchers familiar with PLS-SEM. This novel approach enables researchers to substantiate and improve their theories and helps practitioners disclose the must-have and should-have factors relevant to their decision-making.

/pdf/when-predictors-of-outcomes-are-necessary-guidelines-for-the-58uvx473z0.pdf

Nicole Franziska Richter

Papers

Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM): Eine anwendungsorientierte Einführung

European management research using partial least squares structural equation modeling (PLS-SEM)

A critical look at the use of SEM in international business research

When predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA

Situational job characteristics and job satisfaction: The moderating role of national culture