Organization Structure from a Loose Coupling Perspective: A Multidimensional Approach

Abstract

Organizational theories frequently rely on notions of sharing and dependence among organizational participants, but researchers usually focus on characteristics of the actors themselves instead of the relational patterns among the actors. Loose coupling is one conceptual tool that emphasizes relational patterns. Loose coupling, however, is an abstract metaphor that is simultaneously fertile and ambiguous. This paper develops a rigorous and comprehensive framework that sharpens the theoretical contributions of loose coupling to our understanding of structural relationships. Characteristics of loose coupling capture some important and underexplored features of multidimensional fit and interdependence in organizations. The proposed framework clarifies these theoretical contributions of loose coupling with concepts and equations modified from network analysis. Testable hypotheses are proposed with respect to three key independent variables that may affect patterns of coupling: organization strategy, technology, and environmental turbulence, Additional hypotheses are advanced with respect to the use of the multidimensional approach to loose coupling in studying new organizational forms. Initial psychometric and empirical evidence are presented. Subject Areas: Experimental Design, Interorganizational Linkages, Loose Coupling, Network Theory, Organization Structure, and Organization Theory. *The authors thank Karl Weick, Alkesh Wadwhani, Jack Bnttain, Doug Orton, George Huber, Leo Brady, Jim Judisch, and Greg Ginn for their helpful comments on earlier drafts. We gratefully acknowledge the partial support that IBM provided for this research to the second author under IBM-University of Texas agreement number 482.

DOI: 10.1111/j.1540-5915.2001.tb00959.x

Cite this paper

@article{Beekun2001OrganizationSF, title={Organization Structure from a Loose Coupling Perspective: A Multidimensional Approach}, author={Rafik I. Beekun and William H. Glick}, journal={Decision Sciences}, year={2001}, volume={32}, pages={227-250} }