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Consistent Partial Least Squares Path Modeling1

Theo K. Dijkstra1; Jörg Henseler2,3

1 Faculty of Economics and Business, University of Groningen, Nettelbosje 2, 9747 AE Groningen, The Netherlands · 2 Faculty of Engineering Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede The Netherlands [email protected] · 3 NOVA IMS, Universidade Nova de Lisboa, 1070-312 Lisbon Portugal [email protected]

MIS Quarterly 2015 open access

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.

DOI
10.25300/misq/2015/39.2.02
Volume
39 (2)
Pages
297-316
Language
en
Export
BibTeX
Sources
crossref openalex