Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research (original) (raw)
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South Asian Journal of Social Studies and Economics, 2022
Partial least squares structural equation modeling (commonly referred to as PLS-SEM) was not developed without reason. PLS-SEM was developed as an alternative to covariance-based SEM, allowing researchers to conduct exploratory research. In addition, PLS-SEM is considered capable of providing flexibility related to data characteristics, model complexity, and model specifications. Undoubtedly, PLS-SEM is the most frequently used method in many fields of business research. However, many researchers use PLS-SEM incorrectly and even expect more without understanding the basic structural equation modeling method. For this reason, this article will discuss various types of problems and general beliefs about the use of PLS-SEM in business research. In addition, this article can be used as a reference to make it easier for applied researchers to decide what methods, techniques, and tools will be used to complete their research. In addition, at the end of this article, we will discuss how PLS-SEM can be applied to develop theory in business research through a series of technical introductions taking into account user needs. Subsequently, this article will be equipped with a systematic procedure that discusses the evaluation flow of each PLS-SEM test through illustrations with a notated model using SmartPLS.
An assessment of the use of partial least squares structural equation modeling in marketing research
Journal of The Academy of Marketing Science, 2012
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)
European Business Review, 2014
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 limitation...
Partial Least Squares: The Better Approach to Structural Equation Modeling?
Long Range Planning, 2012
With the ever-increasing acceptance of the need to empirically validate theories in the social science disciplines (e.g., Sheth, 1971), data and multivariate analysis techniques (e.g., Hair et al., 2010; Hair et al., 2011b; Mooi and Sarstedt, 2011) play a central role in today's research. The evolution of structural equation modeling (SEM) methods is perhaps the most important and influential statistical development in the social sciences in recent years. SEM is a second generation multivariate analysis technique that combines features of the first generation techniques, such as principal component and linear regression analysis (Fornell, 1982, 1987). SEM is particularly useful for the process of developing and testing theories and has become a quasi-standard in research (e.g.,
Structural equation modeling and regression: Guidelines for research …
The growing interest in Structured Equation Modeling (SEM) techniques and recognition of their importance in IS research suggests the need to compare and contrast different types of SEM techniques so that research designs can be appropriately selected. After assessing the extent to which these techniques are currently being used in IS research, the article presents a running example which analyzes the same dataset via three very different statistical techniques. It then compares two classes of SEM: covariance-based SEM and partial-least-squaresbased SEM. Finally, the article discusses linear regression models and offers guidelines as to when SEM techniques and when regression techniques should be used. The article concludes with heuristics and rule of thumb thresholds to guide practice, and a discussion of the extent to which practice is in accord with these guidelines. Note: The paper is written in such a way that readers with basic knowledge of multivariate statistics can follow the logic and examples. It does not assume the reader is already conversant with LISREL, PLS, or other SEM tools. This tutorial contains:
Revista Brasileira de Marketing, 2014
Structural equation modeling (SEM) is increasingly a method of choice for concept and theory development in the social sciences, particularly the marketing discipline. In marketing research there increasingly is a need to assess complex multiple latent constructs and relationships. Second-order constructs can be modeled providing an improved theoretical understanding of relationships as well as parsimony. SEM in particular is well suited to investigating complex relationships among multiple constructs. The two most prevalent SEM based analytical methods are covariance-based SEM (CB-SEM) and variance-based SEM (PLS-SEM). While each technique has advantages and limitations, in this article we focus on CB-SEM with AMOS to illustrate its application in examining the relationships between customer orientation, employee orientation, and firm performance. We also demonstrate how higher-order constructs are useful in modeling both responsive and proactive components of customer and employee orientation.
International Journal of Research in Marketing, 2011
The application of structural equation models (SEMs) is common in marketing and the behavioral sciences. Accordingly, the exploration of more effective methods to estimate SEMs is also a popular area of research. and have each proposed a new method for estimating SEMs, but since these proposals nearly a decade ago, these methods have been mostly overlooked by applied researchers. We suggest that reasons for this oversight may include not only a lack of guidance in implementing these new methods but also the absence of a formal comparison to review these new methods relative to the more familiar maximum likelihood structural equation modeling (MLSEM) and partial least squares (PLS). In this paper, our goal was to make the Croon and Skrondal-Laake (SL) methods more accessible to applied researchers. We first provide a step-by-step illustration of how to implement the Croon and SL methods. We also present the first comprehensive evaluation of the new methods relative to MLSEM and PLS. From this evaluation, we can better appreciate the circumstances under which these new methods are preferable to MLSEM and PLS. Thus, we intend to help readers understand how and when to apply these new methods.
Structural equation modeling-A second-generation multivariate analysis
The application of Structural Equation Modeling (SEM) in the business research is growing. The second-generation multivariate data analysis technique, SEM is easy to use and provides a high quality statistical analysis. Many visual SEM software programs help in a quick design of the theoretical model and to modify them graphically using simple drawing tools. Further it can estimate the model’s fit, make any modifications and arrive at a final valid model. It is essential to understand how to design the research process appropriate to the SEM analysis. This article describes the steps in SEM analysis, the conventions used in presenting a model, the elements of the SEM technique, the interpretation of the SEM results. The rule of thumb that evaluates the results and the other issues in SEM reporting are discussed.