Abstract
The partial least squares (PLS) method has been extensively used in information systems research, particularly in the context of PLS-based structural equation modeling (SEM). Nevertheless, our understanding of PLS algorithms and their properties is still progressing. With the goal of improving that understanding, we provide a discussion on the treatment of reflective and formative latent variables in the context of three main algorithms used in PLS-based SEM analyses –PLS regression, PLS Mode A, and PLS Mode B. Two illustrative examples based on actual data are presented. It is shown that the “good neighbor†assumption underlying modes A and B has several consequences, including the following: the inner model influences the outer model in a way that increases inner model coefficients of association and collinearity levels in tandem, and makes measurement model analysis tests dependent on structural model links; instances of Simpson’s paradox tend to occur with Mode B at the latent variable level; and nonlinearity is improperly captured. In spite of these mostly detrimental outcomes, it is argued that modes A and B may have important and yet unexplored roles to play in PLS-based structural equation modeling analyses.
Highlights
The partial least squares (PLS) method has been steadily used in information systems research, in the context of PLS-based structural equation modeling (SEM)
The preceding sections provided a thorough discussion of the treatment of reflective and formative latent variables in the context of three widely used PLS-based SEM algorithms; namely PLS regression, PLS Mode A, and PLS Mode B
Two illustrative examples, based on actual data, highlight some of the key outcomes, including pros and cons, which the use of modes A and B may have on the results of PLS-based SEM analyses, especially when compared with PLS regression
Summary
The partial least squares (PLS) method has been steadily used in information systems research, in the context of PLS-based structural equation modeling (SEM). As an important tool in the information systems researcher’s quantitative analysis arsenal, much has been said about the possible advantages and disadvantages of PLS-based SEM, especially when it is compared with the more traditional covariance-based SEM (Chin, 1998; see, Haenlein & Kaplan, 2004; Recker & La Rosa, 2012) These discussions, which usually revolve around advantages of PLS-based SEM, have been enhanced by cogent arguments suggesting that some advantages have been overstated (Goodhue et al, 2012). The weights linking formative manifest and latent variables must be statistically significant, and collinearity levels among manifest variables must be low The former can be tested through resampling in PLS-based SEM (Chin, 1998; Martin, 2007), while the latter can be tested via the calculation of variance inflation factors and their comparison against a threshold (Diamantopoulos & Siguaw, 2006; Petter et al, 2007). The second reason why this paper is relevant for information systems researchers is that some of the recommendations that follow from the discussion of the three algorithms have direct practical consequences in the context of investment in and use of information technologies, as will be seen later
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
