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generate predictions at the measurable level.” (Shmueli, 2010, p. 293). Depending on the goal of the research, which guides the way the method is applied, PLS-SEM may be positioned closer to the one or the other pole shown in Fig. 2. For example, if the aim is mainly the variance explanation of dependent variables and prediction, emphasis should be put on the evaluation of the model's OLS regression results and its predictive capabilities using corresponding evaluation criteria (Hair et al., 2017; Shmueli, Ray, Velasquez Estrada, & Chatla, 2016). On the contrary, if the analysis focuses on confirmatory/explanatory modeling, researchers should consider the newly proposed consistent PLS approach (Bentler & Huang, 2014; Dijkstra & Henseler, 2015b; Dijkstra, 2014) and the use of PLS-SEM goodness-of-fit criteria (Dijkstra & Henseler, 2015a; Henseler et al., 2014). However, the goal of study usually does not purely follow the one or the other pole of the characterized continuum but takes a position in between. Hence, researchers may consider both sets of evaluation criteria to different degrees. But accomplishing highly satisfactory results in both directions can be difficult since “the ‘wrong’ model can sometimes predict better than the correct one” (Shmueli, 2010, p. 293). The question that arises is the following: Why and when should PLS-SEM be used? Wold (2006) provides, among others, the following key reasons for using PLS-SEM: (a) the PLS-SEM approach has a broad scope and flexibility of theory and practice; and (b) a PLS path model develops through a dialogue between the investigator and the computer, in that tentative model improvementsdsuch as the introduction of a new latent variable, an indicator, and an inner model relation, or the omission of such an elementdare easily and quickly tested for predictive relevance. Moreover, prediction-oriented analyses, complex models, and secondary/archival or big data motivate the use of PLS-SEM (Gefen, Rigdon, & Straub, 2011; Rigdon, 2012, 2014). Additional reasons, suggested by Sarstedt, Ringle, and Hair (2016) and Rigdon (2016), are the use of composites that represent formatively measured latent variables, the use of small sample sizes due to a small population, applying PLS-SEM latent variable scores in subsequent analyses, and endeavoring to overcome factor-based SEM's limitation by mimicking the results of common factor models (i.e., by using consistent PLS approaches; Bentler & Huang, 2014; Dijkstra & Henseler, 2015b).