Consistent partial least squares path modeling

Theo K. Dijkstra, Jörg Henseler

Research output: Contribution to journalReview articlepeer-review

737 Citations (Scopus)


This paper resumes the discussion in information systems research on the use of partial least squares (PLS) path modeling and shows that the inconsistency of PLS path coefficient estimates in the case of reflective measurement can have adverse consequences for hypothesis testing. To remedy this, the study introduces a vital extension of PLS: consistent PLS (PLSc). PLSc provides a correction for estimates when PLS is applied to reflective constructs: The path coefficients, inter-construct correlations, and indicator loadings become consistent. The outcome of a Monte Carlo simulation reveals that the bias of PLSc parameter estimates is comparable to that of covariance-based structural equation modeling. Moreover, the outcome shows that PLSc has advantages when using non-normally distributed data. We discuss the implications for IS research and provide guidelines for choosing among structural equation modeling techniques.

Original languageEnglish
Pages (from-to)297-316
Number of pages20
JournalMIS Quarterly: Management Information Systems
Issue number2
Publication statusPublished - 1 Jun 2015


  • Consistent partial least squares
  • Monte Carlo simulation
  • PLS
  • SEM
  • Variance-based structural equation modeling


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