R&E argue that “using PLS as an exploratory or early-stage theory testing tool does not feature strongly in the early PLS articles” (p. 442) and that only recent literature such as Hair et al. (2011) conveys this notion. This description openly misrepresents research reality. In fact, Herman Wold, the inventor of PLS, emphasized exploratory nature of PLS from the very beginning of its development:
PLS is primarily intended for research contexts that are simultaneously data-rich and theory-skeletal. The model building is then an evolutionary process, a dialog between the investigator and the computer. In the process, the model extracts fresh knowledge from the data, thereby putting flesh on the theoretical bones. At each step PLS rests content with consistency of the unknowns. As the model develops, it may be appropriate to try ML estimation with its higher aspiration of optimal accuracy. (Lohmöller & Wold, 1980, p. 1)
Literature reviews show that only 43 of 306 articles (14.05%) published in marketing, strategic management, and information systems literatures mentioned “exploration” as a rationale for using PLS (Hair, Sarstedt, Pieper, et al., 2012; Hair, Sarstedt, Ringle, et al., 2012; Ringle et al., 2012). This is not all that surprising in light of the academic bias in favor of findings presented in confirmatory terms (Greenwald, Pratkanis, Leippe, & Baumgardner, 1986; Rigdon, 2013). In fact, this observation also holds for covariance-based SEM, which is traditionally viewed as a confirmatory tool. However, its actual use is far from it as researchers routinely engage in specification searches when the initially hypothesized model exhibits inadequate fit. Interestingly, this is more often the rule than the exception (e.g., Cliff, 1983; Sarstedt et al., in press). But does PLS have the necessary requisites for exploratory modeling? Contradicting the view of the originators of PLS, R&E argue that it does not, primarily because PLS cannot be used to validate models. R&E state that whereas covariance-based SEM can detect both overparameterization (by assessing the significance of path coefficients) and underparameterization (using modification indices), PLS can detect only overparameterization. Based on this observation, they conclude that PLS is not suitable for exploratory purposes. We do not agree with this conclusion for three reasons. First, R&E neglect an obvious modeling option: starting the exploratory analysis with a likely overparameterized or even saturated model and dropping nonsignificant paths. Second, as our answer to Critique 3 demonstrated, PLS can certainly be used to validate models, and at least two overall model criteria can detect underparameterization: (a) exact fit and (b) SRMR. Third, R&E fail to empirically demonstrate that covariance-based SEM performs better than PLS in finding the true model. Given the mediocre-to-poor performance of covariance-based SEM specification searches (e.g., Heene, Hilbert, Freudenthaler, & Bühner, 2012; Homburg & Dobratz, 1992), a blind faith in covariance-based SEM’s exploratory capabilities appears both unwise and unjustified.
R&E’s failure to clearly distinguish between the common factor model and the composite factor model also has consequences for their conclusions with respect to PLS’s adequacy for exploratory research. Owing to their blind adherence to the common factor model, R&E misinterpret Lohmöller: “The corollary assumed by Lohmöller, neither logically implied nor correct, is that PLS is appropriate when the researcher is not sure that the model is correct. Lohmöller [implies] that the term explorative refers to situations where the model may be incorrect” (p. 442). If things were really this way, R&E would be justified because model misspecification cannot be corrected by choosing one estimation technique over another (Henseler, 2012). Unfortunately, R&E ignore the fact that Lohmöller refers not to the model (as if there was only one model), but to the common factor model. We have demonstrated that the composite factor model is a less restrictive model than the common factor model. Therefore, Lohmöller’s notion that the composite factor model (as estimated by PLS) is appropriate when the researcher is unsure that the common factor model is correct has obvious merits. Similarly, one should understand Lohmöller’s use of the term explorative: Explorative latent variable path modeling refers to situations where the common factor model may be incorrect.
R&E also discuss a dichotomy of correct versus wrong models without acknowledging that models can be wrong in some subparts only. Particularly in early phases of research, it is difficult to assure that all subparts of a model are entirely correct. Full-information approaches often suffer in such instances because model misspecification in a subpart of a model can have detrimental effects on the rest of the model (Antonakis, Bendahan, Jacquart, & Lalive, 2010). In contrast, limited information methods are more robust to misspecification and are therefore useful for the analysis of initially formulated but misspecified models (Gerbing & Hamilton, 1994). Consequently, there are good reasons to prefer PLS as a limited-information approach over full-information approaches when the correctness of all parts of a model cannot be ensured.
Since PLS estimates a more general model than covariance-based SEM and is less affected by model misspecification in some subparts of the model, it is a suitable tool for exploratory research. We conclude that PLS can be a valuable tool for exploratory research.
Sumber : Journals sagepub