Explaining IS continuance in environments where usage is mandatory
Computers in Human Behavior
Post-Adoption Behaviors of E-Service Customers: The Interplay of Cognition and Emotion
International Journal of Electronic Commerce
Lower bounds on sample size in structural equation modeling
Electronic Commerce Research and Applications
Electronic Commerce Research and Applications
Students' communicative behavior adaptability in CSCL environments
Education and Information Technologies
Editor's comments: PLS: a silver bullet?
MIS Quarterly
A critical look at partial least squares modeling
MIS Quarterly
Enhancing non-task sociability of asynchronous CSCL environments
Computers & Education
An Empirical Investigation of Stress Factors in Information Technology Professionals
Information Resources Management Journal
Information Resources Management Journal
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There is a pervasive belief in the Management Information Systems (MIS) field that Partial Least Squares (PLS) has special abilities that make it more appropriate than other techniques, such as multiple regression and LISREL, when analyzing small sample sizes. We conducted a study using Monte Carlo simulation to compare these three relatively popular techniques for modeling relationships among variables under varying sample sizes (N = 40, 90, 150, and 200) and varying effect sizes (large, medium, small and no effect). The focus of the analysis was on comparing the path estimates and the statistical power for each combination of technique, sample size, and effect size. The results suggest that PLS with bootstrapping does not have special abilities with respect to statistical power at small sample sizes. In fact, for simple models with normally distributed data and relatively reliable measures, none of the three techniques have adequate power to detect small or medium effects at small sample sizes. These findings run counter to extant suggestions in MIS literature.