Organizationally-relevant configurations: the value of modeling local dependence

Organizationally-relevant configurations: the value of modeling local dependence Configurations are important across all levels of organizations. Despite the interest in and importance of configurations in research for organizations, the empirical methods for assessing and classifying configurations has not kept pace with the theoretical advancements. Theory suggests that configurations must include aligned elements that have local dependence. Local dependence is defined as the interrelationships among variables necessary to form an internally consistent configuration. We explain how latent class cluster analysis (LCCA) enables modeling for local dependence and provides theoretical and methodological value for configurations researchers. Using primary data from two samples, we demonstrate that LCCA with local dependence outperforms traditional cluster analysis-based approaches. Our method can be used for detecting configurations at a variety of organizational levels (e.g. nation, industry, firm, and group). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Organizationally-relevant configurations: the value of modeling local dependence

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Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer Science+Business Media B.V.
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-011-9520-3
Publisher site
See Article on Publisher Site

Abstract

Configurations are important across all levels of organizations. Despite the interest in and importance of configurations in research for organizations, the empirical methods for assessing and classifying configurations has not kept pace with the theoretical advancements. Theory suggests that configurations must include aligned elements that have local dependence. Local dependence is defined as the interrelationships among variables necessary to form an internally consistent configuration. We explain how latent class cluster analysis (LCCA) enables modeling for local dependence and provides theoretical and methodological value for configurations researchers. Using primary data from two samples, we demonstrate that LCCA with local dependence outperforms traditional cluster analysis-based approaches. Our method can be used for detecting configurations at a variety of organizational levels (e.g. nation, industry, firm, and group).

Journal

Quality & QuantitySpringer Journals

Published: Nov 12, 2011

References

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