Emergent clustering methods for empirical OM research
概述了运营管理研究中可用的新兴聚类方法，包括其优缺点、软件实现及适用场景，帮助研究者发现传统方法无法揭示的实践启示。
Abstract To date, the vast majority of cluster analysis applications in OM research have relied on traditional hierarchical (e.g., Ward's algorithm) and nonhierarchical (e.g., K ‐means algorithms) methods. Although these venerable methods should continue to be employed effectively in the OM literature, we also believe there is a significant opportunity to expand the scope of clustering methods to emergent techniques. We provide an overview of some alternative clustering procedures (including advantages and disadvantages), identify software programs for implementing them, and discuss the circumstances where they might be employed gainfully in OM research. The implementation of emergent clustering methods in the OM literature should enable researchers to offer implications for practice that might not have been uncovered with traditional methods.