As a fruitful outcome of these debates, substantial contributions to PLS emerged, such as bootstrap-based tests of overall model fit ( Dijkstra and Henseler, 2015a), consistent PLS (PLSc) to estimate factor models (see Dijkstra and Henseler, 2015b), and the heterotrait-monotrait ratio of correlations (HTMT) as a new criterion for discriminant validity (see Henseler et al., 2015). Scholars have discussed the conceptual underpinnings ( Rigdon, 2012, 2014 Sarstedt et al., 2014) as well as the strengths and weaknesses ( Rönkkö and Evermann, 2013 Henseler et al., 2014 Aguirre-Urreta and Marakas, 2013 Rigdon et al., 2014).
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Hair et al., 2012a, b), but just recently it has also undergone a series of serious examinations, and has been the target of heated scientific debates. Not only has PLS and its use been subject of various reviews (cf. Consequently, PLS path modeling is a preferred statistical tool for success factor studies ( Albers, 2010). the abstraction of artifacts such as management instruments, innovations, or information systems. Whereas factors can be used to model latent variables of behavioral research such as attitudes or personality traits, composites can be applied to model strong concepts ( Höök and Löwgren, 2012), i.e. Its ability to model both factors and composites is appreciated by researchers across disciplines, and makes it a promising method particularly for new technology research and information systems research. PLS is widely used in information systems research ( Marcoulides and Saunders, 2006), strategic management ( Hair et al., 2012a), marketing ( Hair et al., 2012b), and beyond. 240) and has been called a “silver bullet” ( Hair et al., 2011). Variance-based SEM is the method of choice if the hypothesized model contains composites.Īmong variance-based SEM methods, partial least squares (PLS) path modeling is regarded as the “most fully developed and general system” ( McDonald, 1996, p. In contrast, variance-based SEM first creates proxies as linear combinations of observed variables, and then estimates the model parameters using these proxies. Covariance-based SEM estimates model parameters using the empirical variance-covariance matrix, and it is the method of choice if the hypothesized model consists of one or more common factors. Two types of SEM can be distinguished: covariance- and variance-based SEM.
Its ability to model latent variables, to take into account various forms of measurement error, and to test entire theories makes it useful for a plethora of research questions. Structural equation modeling (SEM) is a family of statistical techniques that has become very popular in business and social sciences.
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