Chapter 12: Using Partial Least Squares Path Modeling in Advertising Research: Basic Concepts and Recent Issues
Jörg Henseler, Christian M. Ringle and Marko Sarstedt INTRODUCTION Structural equations modeling (SEM) with latent variables has become a quasi-standard statistical method for empirical studies on management and marketing research (Hair et al. 2011a) as well as international advertising research. The desire to test complete theories and concepts is one reason for authors to embrace SEM (Bollen 1989; Henseler et al. 2009). For many marketing and international advertising researchers, SEM is equivalent to carrying out covariance-based structural equation modeling (CB-SEM; e.g., Bagozzi 1994; Diamantopoulos and Siguaw 2000; Rigdon 1998) analyses as supported by statistical software packages such as AMOS, EQS, LISREL, Mplus, and others. In CB-SEM, to estimate a set of model parameters, the difference between the theoretical covariance matrix, implied by the structural equations system for the specified model, and the empirical covariance matrix is minimized. The results permit empirically testing the theoretically developed hypotheses. However, model estimation in CB-SEM requires a large set of assumptions to be fulfilled (i.e., the multivariate normality of the data, the minimum sample size, and others), limiting the approach’s applicability in many research situations. SEM also needs to be thought of as including another unique, and very useful, approach – partial least squares path modeling (PLS; Lohmöller 1989; Wold 1982). Unlike CB-SEM, PLS focuses on maximizing the endogenous latent variables’ explained variance rather than on reproducing the theoretical covariance matrix. If CB-SEM’s premises are violated or if the research objective is prediction rather than comparing competing theories, researchers seem to favor the...
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