Issues and opinion on structural equation modeling
MIS Quarterly
PLS, Small Sample Size, and Statistical Power in MIS Research
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 08
Editor's comments: PLS: a silver bullet?
MIS Quarterly
Opportunities and risks of software-as-a-service: Findings from a survey of IT executives
Decision Support Systems
Internet Technologies, ECRM Capabilities, and Performance Benefits for SMEs: An Exploratory Study
International Journal of Electronic Commerce
Factors Affecting Bloggers' Knowledge Sharing: An Investigation Across Gender
Journal of Management Information Systems
Understanding sustained participation in transactional virtual communities
Decision Support Systems
Journal of the American Society for Information Science and Technology
International Journal of Information Management: The Journal for Information Professionals
Journal of Global Information Management
Factors Affecting Bloggers' Knowledge Sharing: An Investigation Across Gender
Journal of Management Information Systems
Attracted to or Locked In? Predicting Continuance Intention in Social Virtual World Services
Journal of Management Information Systems
Podcasting acceptance on campus: The differing perspectives of teachers and students
Computers & Education
The role of atmospheric cues in online impulse-buying behavior
Electronic Commerce Research and Applications
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Multigroup or between-group analyses are common in the information systems literature. The ability to detect the presence or absence of between-group differences and accurately estimate the strength of moderating effects is important in studies that attempt to show contingent effects. In the past, IS scholars have used a variety of approaches to examine these effects, with the partial least squares (PLS) pooled significance test for multigroup becoming the most common (e.g., Ahuja and Thatcher 2005; Enns et al. 2003; Zhu et al. 2006). In other areas of social sciences (Epitropaki and Martin 2005) and management (Mayer and Gavin 2005; Song et al. 2005) research, however, there is greater emphasis on the use of covariance-based structural equation modeling multigroup analysis. This paper compares these two methods through Monte Carlo simulation. Our findings demonstrate the conditions under which covariance-based multigroup analysis is more appropriate as well as those under which there either is no difference or the component-based approach is preferable. In particular, we find that when data are normally distributed, with a small sample size and correlated exogenous variables, the component-based approach is more likely to detect differences between-group than is the covariance-based approach. Both approaches will consistently detect differences under conditions of normality with large sample sizes. With non-normally distributed data, neither technique could consistently detect differences across the groups in two of the paths, suggesting that both techniques struggle with the prediction of a highly skewed and kurtotic dependent variable. Both techniques detected the differences in the other paths consistently under conditions of non-normality, with the component-based approach preferable at moderate effect sizes, particularly for smaller samples.