Market basket analysis (MBA), also known as association rule mining or affinity analysis, is a data-mining technique that originated in the field of marketing and more recently has been used effectively in other fields, such as bioinformatics, nuclear science, pharmacoepidemiology, immunology, and geophysics. The goal of MBA is to identify relationships (i.e., association rules) between groups of products, items, or categories. We describe MBA and explain that it allows for inductive theorizing; can address contingency (i.e., moderated) relationships; does not rely on assumptions such as linearity, normality, and residual equal variance, which are often violated when using general linear model–based techniques; allows for the use of data often considered "unusable" and "messy" in management research (e.g., data not collected specifically for research purposes); can help build dynamic theories (i.e., theories that consider the role of time explicitly); is suited to examine relationships across levels of analysis; and is practitioner friendly. We explain how the adoption of MBA is likely to help bridge the much-lamented micro–macro and science–practice divides. We also illustrate that use of MBA can lead to insights in substantive management domains, such as human resource management (e.g., employee benefits), organizational behavior (e.g., dysfunctional employee behavior), entrepreneurship (e.g., entrepreneurs’ identities), and strategic management (e.g., corporate social responsibility). We hope our article will serve as a catalyst for the adoption of MBA as a novel methodological approach in management research.