Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy C-means method for strategic grouping
Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy C-means method for strategic grouping
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DOI：
10.1016/j.eswa.2009.04.022
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发表时间：
2009-11-01
影响因子：
8.5
通讯作者：
Birgonul, M. Talat
中科院分区：
文献类型：
--
作者：
Budayan, Cenk;Dikmen, Irem;Birgonul, M. Talat
战略群体分析包括根据企业在一系列战略维度上的相似性对行业内的企业进行聚类，并调查战略群体成员资格对绩效的影响。战略群体分析的挑战之一是聚类方法的选择。在本研究中，提出了土耳其承包商的战略分组分析结果，以比较传统聚类分析技术、自组织映射（SOM）和模糊 C 均值方法（FCM）的战略分组性能。研究结果表明，传统的聚类分析方法无法揭示重叠的战略集团结构以及同一战略集团内公司的地位。结论是SOM和FCM比传统的聚类分析更能揭示战略群体的类型，并且更有可能提供有关真实战略群体结构的有用信息。 (C) 2009 Elsevier Ltd. 保留所有权利。
Strategic group analysis comprises of clustering of firms within an industry according to their similarities with respect to a set of strategic dimensions and investigating the performance implications of strategic group membership. One of the challenges of strategic group analysis is the selection of the clustering method. In this study, the results of the strategic group analysis of Turkish contractors are presented to compare the performances of traditional cluster analysis techniques, self-organizing maps (SOM) and fuzzy C-means method (FCM) for strategic grouping. Findings reveal that traditional cluster analysis methods cannot disclose the overlapping strategic group structure and position of companies within the same strategic group. It is concluded that SOM and FCM can reveal the typology of the strategic groups better than traditional cluster analysis and they are more likely to provide useful information about the real strategic group structure. (C) 2009 Elsevier Ltd. All rights reserved.