Multigroup analysis in information systems research using PLS-PM: A systematic investigation of approaches

Michael Klesel, Florian Schuberth, Björn Niehaves, Jörg Henseler

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)
77 Downloads (Pure)

Abstract

Heterogeneity is a pertinent issue in Information Systems (IS) research because human behavior often differs across groups. In the partial least squares path modeling (PLS-PM) context, several approaches have been proposed to investigate potential group differences. Despite the availability of numerous approaches, literature that compares their efficacy is sparse. Consequently, IS researchers lack guidance on which approach is best suited to detect group differences. We address this issue by presenting the results of an extensive Monte Carlo simulation study that juxtaposes the various approaches' behavior under numerous conditions. In doing so, we first provide an overview on existing approaches proposed for multigroup analysis (MGA) in the PLS-PM context. Moreover, we derive important implications for applied research: Firstly, we show that the omnibus test of group differences (OTG) and approaches based on the comparison of confidence intervals are not recommendable for MGA. Secondly, we provide detailed information as to which approaches are suitable for comparing one specific path coefficient and which are recommended if the complete structural model is compared across groups. Finally, we show that approaches which are designed to compare a single parameter require an adjustment for multiple comparisons when used to compare more than two groups.
Original languageEnglish
Pages (from-to)26-48
Number of pages23
JournalData Base for Advances in Information Systems
Volume53
Issue number3
Early online date26 Jul 2022
DOIs
Publication statusPublished - Aug 2022

Keywords

  • Multigroup analyis
  • Monte Carlo simulation
  • Permutation test
  • Model comparison
  • Partial least squares path modeling
  • 22/3 OA procedure

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