Decision making using neural networks: an application to cross-cultural management

Clustering various countries according to their relative similarity in terms of relevant organizational variables is a very useful management tool for multinational enterprises(MNEs). The effects of the nature of population and type of “similarity” variables on the cluster compositions are generally well understood. However, the differences on cluster compositions arising from the underlying differences of various techniques have not been well investigated. This paper is the first empirical study using neural networks (specifically Kohonen´s SOFM) as a tool to identify country clusters based on managers´ perceptions of various management and human resource development(HRD) practices in a large MNE. A method of obtaining near-optimum number of country clusters is described. The clusters developed by the SOFM network are also compared with those obtained using a popular clustering technique such as Q-factor analysis