A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and Voice Mail Emotion/Adoption Study

The ability to detect and accurately estimate the strength of interaction effects are critical issues that are fundamental to social science research in general and IS research in particular. Within the IS discipline, a large percentage of research has been devoted to examining the conditions and contexts under which relationships may vary, often under the general umbrella of contingency theory ( McKeen, Guimaraes, and Wetherbe 1994; Weill and Olson 1989). In our survey of such studies where such moderating variables are explored, a majority fail to either detect and/or provide an estimate of the effect size. In cases where effects sizes are estimated, the numbers are generally small. These results have, in turn, led some to question the usefulness of contingency theory and the need to detect interaction effects (e.g., Weill and Olson 1989). This paper addresses this issue by providing a new latent variable modeling approach that can give more accurate estimates of such interaction effects by accounting for the measurement error in measures which attenuates the estimated relationships. The feasibility of this approach at recovering the true effects is demonstrated in two studies: a simulated data set where the underlying true effects are known and a Voice Mail adoption data set where the emotion of enjoyment is shown to have both a substantial direct and interaction effect on adoption intention.