Moderated regression models (MRMs) are widely used in business research to investigate how the relationship between a predictor and an outcome changes across different levels of a moderator. A common issue in MRM is multicollinearity, and mean-centering is often applied to address this problem, though its effectiveness is debated. This study reveals that the t-test for the main effect of the focal predictor at the mean of a moderator in a mean-centered MRM is equivalent to testing the overall simple effect of the predictor as a population-level effect, independent of specific moderator levels. This insight provides a fresh perspective on the utility of mean-centering in MRMs. Beyond mitigating multicollinearity, mean-centering offers a practical and efficient approach for understanding the average impact of a predictor across varying contextual conditions. By assessing both main effects and interaction effects within a single model, researchers can derive more nuanced insights into complex business phenomena, ultimately supporting more informed strategic decision-making.