The application of SOM as a decision support tool to identify AACSB peer schools

Authors:
Melody Y. Kiang;Dorothy M. Fisher;Jeng-Chung Victor Chen;Steven A. Fisher;Robert T. Chi
Affiliations:
Department of Information Systems, College of Business Administration, California State University, Long Beach, United States and School of Management, Harbin Institute of Technology, Heilongjiang ...;Department of Information Systems and Operations Management, College of Business Administration and Public Policy, California State University, Dominguez Hills, United States;Department of Transportation and Communication Management Science, National Cheng Kung University, Taiwan;Department of Accountancy, College of Business Administration, California State University, Long Beach, United States;Department of Information Systems, College of Business Administration, California State University, Long Beach, United States
Venue:
Decision Support Systems
Year:
2009

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Abstract

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.