Using Confirmatory Composite Analysis to Assess Emergent Variables in Business Research

Jörg Henseler*, Florian Schuberth

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

93 Citations (Scopus)
194 Downloads (Pure)


Confirmatory composite analysis (CCA) was invented by Jörg Henseler and Theo K. Dijkstra in 2014 and elaborated by Schuberth et al. (2018b) as an innovative set of procedures for specifying and assessing composite models. Composite models consist of two or more interrelated constructs, all of which emerge as linear combinations of extant variables, hence the term ‘emergent variables’. In a recent JBR paper, Hair et al. (2020) mistook CCA for the measurement model evaluation step of partial least squares structural equation modeling. In order to clear up potential confusion among JBR readers, the paper at hand explains CCA as it was originally developed, including its key steps: model specification, identification, estimation, and assessment. Moreover, it illustrates the use of CCA by means of an empirical study on business value of information technology. A final discussion aims to help analysts in business research to decide which type of covariance structure analysis to use.
Original languageEnglish
Pages (from-to)147-156
Number of pages10
JournalJournal of business research
Early online date11 Aug 2020
Publication statusPublished - 1 Nov 2020


  • CCA
  • Composite model
  • Confirmatory composite analysis
  • Covariance structure analysis
  • Emergent variables
  • Structural equation modeling


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