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Today, there is heightened controversy about the value of partial least squares (PLS) path modeling as a quantitative research method, including within the domain of European management research. Critical lines of argument within the management and psychology literature assert that there is no reason to use PLS path modeling at all. At the same time, authors using PLS path modeling continue to advance fallacious arguments to justify their choice of method. This paper identifies flaws on both sidesdinvalid arguments in favor of using PLS path modeling and invalid arguments opposing its usedwithin the context of a unifying framework and a realist philosophy of science.
As a family of statistical methods, structural equation modeling is still young. The factor-based approach to SEM spread rapidly across the social sciences (Bentler, 1986), while PLS path modeling emerged later and developed more slowly, perhaps due to the absence of strong software. Regardless, much of the “received wisdom” on SEM has a limited evidentiary base for support. More than that, the emergence of SEM invited researchers to think in new ways, at new levels of abstraction. Analogies and heuristics are powerful tools in helping people to come to grips with things that are new and difficult to understand. While useful, however, heuristics can also be misleading. The more fundamental technology of null hypothesis significance testing is itself widely misunderstood, with textbooks enshrining mistakes as basic principles and infecting whole generations of researchers with falsehoods and confusion (Gigerenzer, 2004; Ziliak & McCloskey, 2008). Regarding factor analysis, Stewart (1981, p. 51) noted a similar state: “So widespread are current misconceptions about factor analysis in the marketing community that even its defenders and some prominent reviewers perpetuate misinformation.” The same might be said of both factor-based and composite-based approaches to SEM today. Saunders and Bezzina's (2015) “methodological tribalism” appears to separate quantitative methods communities just as much as it separates quantitative from qualitative