Sarstedt, MarkoMarkoSarstedtRingle, Christian M.Christian M.Ringle2019-12-122019-12-122010-08-20Journal of Applied Statistics 37 (8): 1299-1318 (2010-08-20)http://hdl.handle.net/11420/4053In the social science disciplines, the assumption that the data stem from a single homogeneous population is often unrealistic in respect of empirical research. When applying a causal modeling approach, such as partial least squares path modeling, segmentation is a key issue in coping with the problem of heterogeneity in the estimated cause-effect relationships. This article uses the novel finite-mixture partial least squares (FIMIX-PLS) method to uncover unobserved heterogeneity in a complex path modeling example in the field of marketing. An evaluation of the results includes a comparison with the outcomes of several data analysis strategies based on a priori information or k-means cluster analysis. The results of this article underpin the effectiveness and the advantageous capabilities of FIMIX-PLS in general PLS path model set-ups by means of empirical data and formative as well as reflective measurement models. Consequently, this research substantiates the general applicability of FIMIX-PLS to path modeling as a standard means of evaluating PLS results by addressing the problem of unobserved heterogeneity.en0266-47632010812991318Corporate reputationFinite mixtureHeterogeneityLatent classMarket segmentationPartial least square (pls)Path modelingAllgemeines, WissenschaftJournal Article10.1080/02664760903030213Other