Conflating Antecedents and Formative Indicators: A Comment on Aguirre-Urreta and Marakas

Author(s)
Edward E. Rigdon, Jan-Michael Becker, Arun Rai, Christian M. Ringle, Adamantios Diamantopoulos, Elena Karahanna, Detmar W. Straub, Theo K. Dijkstra
Abstract

Aguirre-Urreta and Marakas [Aguirre-Urreta MI, Marakas GM (2014) Research note—Partial least squares and models with formatively specified endogenous constructs: A cautionary note. Inform. Systems Res. 25(4):761–778] aim to evaluate the performance of partial least squares (PLS) path modeling when estimating models with formative endogenous constructs, but their ability to reach valid conclusions is compromised by three major flaws in their research design. First, their population data generation model does not represent “formative measurement” as researchers generally understand that term. Second, their design involves a PLS path model that is misspecified with respect to their population model. Third, although their aim is to estimate a composite-based PLS path model, their design uses simulation data generated via a factor analytic procedure. In consequence of these flaws, Aguirre-Urreta and Marakas' (2014) study does not support valid inference about the behavior of PLS path modeling with respect to endogenous formatively measured constructs.

Organisation(s)
Department of Accounting, Innovation and Strategy
External organisation(s)
Georgia State University, Universität zu Köln, University of Newcastle, Technische Universität Hamburg-Harburg (TUHH), University of Groningen
Journal
Information Systems Research
Volume
25
Pages
780-784
No. of pages
5
ISSN
1047-7047
DOI
https://doi.org/10.1287/isre.2014.0543
Publication date
09-2014
Peer reviewed
Yes
Austrian Fields of Science 2012
502052 Business administration
Keywords
ASJC Scopus subject areas
Information Systems and Management, Information Systems, Library and Information Sciences, Management Information Systems, Computer Networks and Communications
Portal url
https://ucrisportal.univie.ac.at/en/publications/055e98ae-7dbb-410c-96b0-c601c9a2331b