Theory within organizational research has increasingly mobilized the notion of conceptual spaces—multidimensional spaces within which shared concepts relate, combine, disperse or compete with one another. We here show how conceptual spaces can be empirically represented and measured through word embedding models, a recent advance in natural language processing and machine learning that now permits scholars to efficiently encode complex systems of meanings with less semantic distortion than before. In so doing, we have a twofold aim. First, to provide the theoretical notion of conceptual spaces with a rigorous and scalable representation and measurement framework, and second, to provide the word embedding family of computational models with a stable conceptual framework upon which its output can be interpreted with theoretical significance by organizational scientists. We start by laying out an explicit definition of conceptual spaces and illustrating their key features. We then describe how embedding models serve as a powerful framework for representing them, describing how these models work and walking the reader through the code required to implement them. Drawing on recent work within organization science and neighboring social sciences, we then proceed to illustrate what we see as a promising set of conceptual space measures, which allow us to theorize and formally trace theoretical constructs based on geometric notions of proximity and distance, breadth and depth, similarity, dimensionality, and hierarchy. Through practical code implementations of these measures, we illustrate how conceptual spaces as represented in embedding models can fruitfully open new and expand existing theoretical terrain in a range of fields and increase the fidelity of current measures.

Word embedding models are a powerful approach for representing the multidimensional conceptual spaces within which communicated concepts relate, combine, and compete with one another. This class of models represent a recent advance in machine learning allowing scholars to efficiently encode complex systems of meaning with minimal semantic distortion based on local and global word co-occurrences from large-scale text data. Although their use has the potential to broaden theoretical possibilities within organization science, embeddings are largely unknown to organizational scholars, where known they have only been mobilized for a narrow set of uses, and they remain unlinked to a theoretical scaffolding that can enable cumulative theory building within the organizations community. Our goal is to demonstrate the promise embedding models hold for organization science by providing a practical roadmap for users to mobilize the methodology in their research and a theoretical guide for consumers of that research to evaluate and conceptually link embedded representations with theoretical significance and potential. We begin by explicitly defining the notions of concept and conceptual space before proceeding to show how these can be represented and measured with word embedding models, noting strengths and weaknesses of the approach. We then provide a set of embedding measurements along with their theoretical interpretation and flexible extension. Our aim is to extract the operational and conceptual significance from technical treatments of word embeddings and place them within a practical, theoretical framework to accelerate research committed to understanding how individuals, teams, and broader collectives represent, communicate, and deploy meaning in organizational life. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.1686 .