Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS (original) (raw)
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Partial least squares structural equation modeling (PLS-SEM)
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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...
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): An emerging tool in business research
European Business Review, 2014
Purpose: We 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 we merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, we 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 non-normal 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 research on the PLS...
STRUCTURAL EQUATION MODELING WITH THE SMARTPLS
The objective of this article is to present a didactic example of Structural Equation Modeling using the software SmartPLS 2.0 M3. The program mentioned uses the method of Partial Least Squares and seeks to address the following situations frequently observed in marketing research: Absence of symmetric distributions of variables measured by a theory still in its beginning phase or with little “consolidation”, formative models, and/or a limited amount of data. The growing use of SmartPLS has demonstrated its robustness and the applicability of the model in the areas that are being studied.
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.
(CHAPTER 1)REVIEW ON PARTIAL LEAST SQUARE STRUCTURAL EQUATION MODELING (PLS-SEM) USING SMARTPLS 2.0
Structural Equation Modeling (SEM) are complex models allowing us to study the real world complexity by taking into account a whole number of causal relationships among latent concept (i.e. the Latent variables, LVs), each measured by several observed indicators usually defined as Manifest Variables (Esposito Vinzi, 2014) Covariance-based/ Factor based Methods: 1. The aim is to reproduce the sample covariance matrix of the manifest variables by means of the model parameters: The implied covariance matrix of the manifest variable is a function of the model parameters It is a confirmatory approach aiming at the validating a model (theory building) The latent variables are equivalent to common factors (Theoretical and random variables) Variance based/ Composite based / Component based Method 2. The aim is to provide latent variable scores (proxy, composites, factor scores) that are the most correlated to each other as possible and the most representative of their own block of manifest variables It focuses on latent variable scores computation It focuses on explaining variances It is more an explanatory approach than a confirmatory one (operational model strategy) The latent variables are defined as components or weighted sums of the manifest variables (Fixed variables: linear composites, scores) This leads to different parameters to estimate for latent variables Factor means and variances in covariance based methods Weight in component based approaches 1.2 THE CONCEPT OF PLS-SEM AND HOW IT WORKS Today, Partial Least Square Structural Equation Modeling (PLS-SEM) has gaining interesting in statistical inferential to help the researchers construct the complex model CB-SEM (Covariance based SEM) -This method is aimed to reproduced the theoretical covariance matrix, without focusing on explained variance as implemented in AMOS package.
Structural Equation Modeling with the Smartpls [in English]
REMark - Revista Brasileira de Marketing, 2014
The objective of this article is to present a didactic example of Structural Equation Modeling using the software SmartPLS 2.0 M3. The program mentioned uses the method of Partial Least Squares and seeks to address the following situations frequently observed in marketing research: Absence of symmetric distributions of variables measured by a theory still in its beginning phase or with little "consolidation", formative models, and/or a limited amount of data. The growing use of SmartPLS has demonstrated its robustness and the applicability of the model in the areas that are being studied.
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.
Partial Least Squares: The Better Approach to Structural Equation Modeling?
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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.,