期刊名称：Journal of International Technology and Information Management
印刷版ISSN：1941-6679
出版年度：2016
卷号：15
期号：2
页码：4
出版社：California State University, San Bernardino
摘要：This paper applies artificial intelligence (AI) computing, Kohonan Self-Organizing Maps (SOMs), to the problem of international market selection (IMS). Broadly speaking, IMS can be summarized to consist of three stages a) screening stage, b) identification stage and, c) selection stage. The screening stage often employs some type of grouping technique so that firms can begin to view the potential markets in terms of similarities and differences over variables of interest. The underlying purpose is to screen out or eliminate markets that do not meet certain criteria established by the firm. Statistical techniques such as cluster analysis, discriminant analysis or factor analysis have long been employed at this stage. This study uses empirical data to demonstrate how an AI approach can assist international firms in the screening process and provide them with information that is not readily available by standard statistical techniques.
