Variance-Based Structural Equation Modeling: Guidelines for Using Partial Least Squares in Information Systems Research

Variance-Based Structural Equation Modeling: Guidelines for Using Partial Least Squares in Information Systems Research

José L. Roldán (Universidad de Sevilla, Spain) and Manuel J. Sánchez-Franco (Universidad de Sevilla, Spain)
DOI: 10.4018/978-1-4666-0179-6.ch010
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Abstract

Partial Least Squares (PLS) is an efficient statistical technique that is highly suited for Information Systems research. In this chapter, the authors propose both the theory underlying PLS and a discussion of the key differences between covariance-based SEM and variance-based SEM, i.e., PLS. In particular, authors: (a) provide an analysis of the origin, development, and features of PLS, and (b) discuss analysis problems as diverse as the nature of epistemic relationships and sample size requirements. In this regard, the authors present basic guidelines for the applying of PLS as well as an explanation of the different steps implied for the assessment of the measurement model and the structural model. Finally, the authors present two examples of Information Systems models in which they have put previous recommendations into effect.
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Structural Equation Modeling In Information Systems Research

From a philosophical position, research that applies Structural equation modeling (SEM) usually follows a positivist epistemological belief (Urbach & Ahlemann, 2010). In this vein, SEM emerges as a result of the conjunction of two traditions (Chin 1998a). On the one hand, an econometric perspective (linear regression models), on the other hand, a psychometric approach (factor analysis). SEM thus combines the usage of latent (unobserved) variables that represent the concepts of theory, and data from measures (indicators or manifest variables) that are used as input for statistical analysis that provides evidence about the relationships among latent variables (Williams, Vandeberg, & Edwards, 2009). SEM is particularly useful in IS research, where many if not most of the key concepts are not directly observable. Indeed, a large portion of IS research during recent years has mainly applied SEM as an analytical methodology for theory testing (Gefen, et al., 2000; Gerow et al., 2010). In turn, an expansion to the breadth of application of SEM methods can be also noted, including exploratory, confirmatory and predictive analysis, as well as the generating of an increasing diversity of ad hoc topics and models (Westland, 2010).

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