Kennesaw State University

About: Kennesaw State University is a education organization based out in Kennesaw, Georgia, United States. It is known for research contribution in the topics: Population & Computer science. The organization has 2869 authors who have published 6266 publications receiving 138679 citations. The organization is also known as: KSU.

PLS-SEM: Indeed a Silver Bullet

Abstract: Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm, and provides an overview of when it can be most appropriately applied, indicating its potential and limitations for future research. The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.

An assessment of the use of partial least squares structural equation modeling in marketing research

Abstract: Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research—despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also give an overview of the interdependencies between researchers’ choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.

Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research

Abstract: Purpose – The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields Design/methodology/approach – In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage Findings – PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity Research limitations/implications – While rese

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

Abstract: 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.

Partial Least Squares Structural Equation Modeling

Abstract: 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.