Overstating and understating interaction results in international business research

Approximately one-third of international business (IB) articles include conditional hypotheses, yet the vast majority risk errors in testing or interpreting the results. Scholars typically restrict their empirical analysis to the coefficient of the interaction term in the regression, exposing themselves to the hazard of overstating or understating results. To mitigate the risk of misstating, we advocate that IB scholars also evaluate the statistical significance of the marginal effect of the primary independent variable over the range of values of the moderating variable. We demonstrate that overstating results can occur when the interaction term coefficient is statistically significant but the marginal effect is not significantly different from zero for some value(s) of the moderating variable. Understating can occur when the interaction term coefficient is not statistically significant, but the marginal effect is statistically different from zero for some value(s) of the moderating variable. In this article, we describe, using simulated data, these two possibilities associated with testing conditional hypotheses, and offer practical guidance for IB scholars.