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 Jörg 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 later-published 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.
- Composite models
- Confirmatory composite analysis
- Emergent variables
- Partial least squares structural equation modeling
- Structural equation modeling