A clarification of confirmatory composite analysis (CCA)

Confirmatory composite analysis (CCA) is a structural equation modeling (SEM) technique that specifies and assesses composite models. In a composite model, the construct emerges as a linear combination of observed variables. CCA was invented by Jo center dot rg Henseler and Theo K. Dijkstra in 2014, was subsequently fully elaborated by Schuberth et al. (2018), and was then introduced into business research by Henseler and Schuberth (2020b). Inspired by Hair et al. (2020), a recent article in the International Journal of Information Management (Motamarri et al., 2020) used the same term 'confirmatory composite analysis' as a technique for confirming measurement quality in partial least squares structural equation modeling (PLS-SEM) specifically. However, the original CCA (Henseler et al., 2014; Schuberth et al., 2018) and the Hair et al. (2020) technique are very different methods, used for entirely different purposes and objectives. So as to not confuse researchers, we advocate that the laterpublished Hair et al. (2020) method of confirming measurement quality in PLS-SEM be termed 'method of confirming measurement quality' (MCMQ) or 'partial least squares confirmatory composite analysis' (PLS-CCA). We write this research note to clarify the differences between CCA and PLS-CCA.

Automated summary

Confirmatory composite analysis (CCA) is a technique within structural equation modeling (SEM) used to assess composite models, with the original CCA being distinct from recent methods proposed by Hair et al. (2020) for confirming measurement quality in PLS-SEM; To avoid confusion, researchers suggest using different terminology to differentiate between the two methods.