The Kohonen self‐organizing map: an application to the study of strategic groups in the UK hotel industry

This paper examines a neural network method known as the self‐organizing map, to transform the data to a two‐dimensional grid of nodes while preserving its ’topological’ structure, and compares and contrast the results of a standard cluster analysis using the k‐means method.

Abstract

This paper examines a neural network method known as the self‐organizing map (SOM). The motivation behind the SOM is to transform the data to a two‐dimensional grid of nodes while preserving its ’topological’ structure. In neural network terminology this involves unsupervised learning. The nearest related statistical technique is cluster analysis. We employ the SOM in the task of identifying strategic groups of companies, using data which relate to the generic strategies suggested by Porter. Following identification of different groups of hotels with certain strategic emphases, the study investigates correlations between strategies followed and hotel performance. We compare and contrast the ’feature map’ generated by the SOM with the results of a standard cluster analysis using the k‐means method. The data also cover performance indicators and the results indicate that performance varies between strategic groups.