How to address endogeneity in partial least squares path modeling

Jose Benitez, Jörg Henseler, José L. Roldán

Research output: Contribution to conferencePaperpeer-review

36 Citations (Scopus)
202 Downloads (Pure)

Abstract

Some of the models using partial least squares (PLS) in Information Systems (IS) field may have serious problems because do not properly address endogeneity. This may suppose a problem in IS theory building because it may lead IS scholars to non-correct results. Although the IS community's awareness is rising, we do not have a clear understanding of the problem nor fine-grained practical guidelines on how to address the endogeneity in IS empirical research using PLS. Further, none of the PLS software packages has test of endogeneity capabilities. This paper explains and illustrates how to address endogeneity in research using PLS path modeling, and contribute to IS research in two ways: (1) we define the problem of endogeneity in empirical research and explain its main causes with IS research examples, (2) we show how to address endogeneity by correcting for omitted variables in PLS path modeling with composite and factor models.

Original languageEnglish
Publication statusPublished - 2016
Event22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016 - San Diego, United States
Duration: 11 Aug 201614 Aug 2016
Conference number: 22

Conference

Conference22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016
Abbreviated titleAMCIS 2016
Country/TerritoryUnited States
CitySan Diego
Period11/08/1614/08/16

Keywords

  • And reverse causality
  • Endogeneity
  • Omitted variables
  • Partial least squares

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