Thereby, researchers ensure that the measurement models in their studies capture what they intend to measure (Campbell and Fiske 1959). The estimated strength of these relationships, most notably between the latent variables, can only be meaningfully interpreted if construct validity was established (Peter and Churchill 1986). Variance-based SEM methods-such as partial least squares path modeling (PLS Lohmöller 1989 Wold 1982), generalized structured component analysis (GSCA Henseler 2012 Hwang and Takane 2004), regularized generalized canonical correlation analysis (Tenenhaus and Tenenhaus 2011), and best fitting proper indices (Dijkstra and Henseler 2011)-have in common that they employ linear composites of observed variables as proxies for latent variables, in order to estimate model relationships. 2011 Rigdon 2014 Tenenhaus and Tenenhaus 2011), as well as its frequent application across different disciplines, demonstrate (e.g., Hair et al. Variance-based structural equation modeling (SEM) is growing in popularity, which the plethora of recent developments and discussions (e.g., Henseler et al.
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