Structural equation modeling (SEM) has become a quasi-standard in marketing research when it comes to analyzing the cause-effect relationships between latent variables. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CBSEM) or variance-based partial least squares (PLS), whose performance has been researched in a multitude of simulation studies. However, recent research has brought forward a variety of different methods for estimating structural equation models, which have not been researched in-depth. We extend prior research by (1) examining a broad range of SEM methods, several of which have not been analyzed in-depth in prior research (CBSEM (Jöreskog 1978), PLS (Wold 1982), extended PLS (PLSe; Lohmöller 1979), consistent PLS (PLSc; Dijkstra and Henseler 2015), generalized structured component analysis (GSCA; Hwang and Takane 2004), and sum scores), (2) analyzing null relationships in the structural model, (3) considering measurement model results, and (4) reporting additional performance measures that allow a nuanced assessment of the results.
