This study enhances stance detection on social media by incorporating deeper psychological attributes, specifically individuals’ moral foundations. These theoretically-derived dimensions aim to provide an interpretable profile of an individual’s moral concerns which, in recent work, has been linked to behaviour in a range of domains including society, politics, health, and the environment. In this paper, we investigate how moral foundation dimensions can contribute to detecting an individual’s stance on a given target. Specifically, we incorporate moral foundation features extracted from text, along with semantic features, to classify stances at both message- and user-levels using traditional machine learning and Large Language Models (LLMs). Our preliminary results suggest that encoding moral foundations can enhance the performance of stance detection tasks, but with notable heterogeneity across task type, models, and datasets. In addition, we illustrate meaningful associations between specific moral foundations and online stances on target topics. The findings from this study highlight the importance of considering deeper psychological attributes in stance classification tasks, and underscore the role of moral foundations in guiding online social behavior.