For a business school, the selection of its peer schools is an important component of its International Association for Management Education (AACSB) (re)accreditation process. A school typically compares itself with other institutions having similar structural and identity-based attributes. The identification of peer schools is critical and can have a significant impact on a business school's accreditation efforts. For many schools the selection of comparable peer schools is a judgmental process. This study offers an alternative means for selection; a quantitative technique called Kohonen's Self-Organizing Map (SOM) network for clustering. In this research, we first demonstrate the capability of SOM as a clustering tool to visually uncover the relationships among AACSB-accredited schools. The results suggest that SOM is an effective and robust clustering method. Then, we compare the results of SOM with that of other clustering methods, such as K-means, Factor/K-means analysis, and kth nearest neighbor procedure. The objective of this study is to demonstrate that a two-dimensional SOM map can be used to integrate the results of various clustering methods and, thus, act as a visual decision support tool.
All Science Journal Classification (ASJC) codes
- Management Information Systems
- Information Systems
- Developmental and Educational Psychology
- Arts and Humanities (miscellaneous)
- Information Systems and Management