Multi-Stage Clustering with Complementary Structural Analysis of 2-Mode Networks

This paper offers a synthesis of a new analytical procedure based on the complementary use of a large number of methods and techniques for categorization of objects, pattern recognition and for structural analysis. It represents an example of a functional clustering [1] and an extension to the ‘posteriori methods’ for clusterisation [2]. We call this approach Multi-Stage Clustering (MSC), as it applies cluster analysis methods at three distinctive stages. We present the MSC and demonstrate its application to a business data set of 275 multinational corporations (MNCs), aiming to address the inherent weaknesses of existing industrial classification tools designed to capture diversification of firms. We evaluate the outcomes from the MSC using a combination of complementary methods for structural analysis and data visualization, such as multi-dimensional scaling (MDS), network mapping (NM) and multiple correspondence analysis (MCA). The MSC is designed for the analysis of diversification patterns of MNCs, which can enable the measurement of group competitiveness and performance across these patterns, known as industry segments, or strategic industry groups (SIGs)