TY - JOUR
T1 - Multigroup Analysis in Information Systems Research using PLS-PM
AU - Klesel, Michael
AU - Schuberth, Florian
AU - Niehaves, Björn
AU - Henseler, Jörg
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT#
Klesel, M., Schuberth, F., Niehaves, B., & Henseler, J. (2022). Multigroup Analysis in Information Systems Research using PLS-PM. Data Base for Advances in Information Systems - ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 53(3), 26-48. https://doi.org/10.1145/3551783.3551787 ---%ABS2%---Funding Information: Jörg Henseler gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020). Moreover, he acknowledges a financial interest in ADANCO and its distributor, Composite Modeling.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - 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.
AB - 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.
KW - distance-based permutation test
KW - monte carlo simulation
KW - multigroup analysis
KW - omnibus test of group differences (otg)
KW - partial least squares path modeling
UR - http://www.scopus.com/inward/record.url?scp=85135048060&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000836062500003
U2 - 10.1145/3551783.3551787
DO - 10.1145/3551783.3551787
M3 - Article
AN - SCOPUS:85135048060
SN - 0095-0033
VL - 53
SP - 26
EP - 48
JO - Data Base for Advances in Information Systems
JF - Data Base for Advances in Information Systems
IS - 3
ER -