Dynamics of Organizational Routines: A Generative Model

25 Pages Posted: 17 Nov 2012

See all articles by Brian Pentland

Brian Pentland

Michigan State University - Department of Accounting & Information Systems

Martha S. Feldman

University of Michigan at Ann Arbor - Gerald R. Ford School of Public Policy

Markus C. Becker

University of Southern Denmark

Peng Liu

affiliation not provided to SSRN

Date Written: December 2012

Abstract

This paper introduces a generative model of organizational routines and their change over time. The model demonstrates that variation and selective retention of patterns of action are necessary and sufficient to explain the features of organizational routines that are most relevant in relation to dynamic capabilities, such as formation, inertia, endogenous change, and learning. The model directly links micro‐level actions to the macro‐level dynamics of routines. The results suggest that focusing on action provides a useful and parsimonious foundation for a theory of organizational routines and capabilities.

Keywords: organizational capabilities, organizational routines, path dependence, patterns of action, sociomaterial systems, variation and selective retention

Suggested Citation

Pentland, Brian and Feldman, Martha S. and Becker, Markus C. and Liu, Peng, Dynamics of Organizational Routines: A Generative Model (December 2012). Journal of Management Studies, Vol. 49, Issue 8, pp. 1484-1508, 2012, Available at SSRN: https://ssrn.com/abstract=2177113 or http://dx.doi.org/10.1111/j.1467-6486.2012.01064.x

Brian Pentland

Michigan State University - Department of Accounting & Information Systems ( email )

270 North Business Complex
East Lansing, MI 48824-1034
United States

Martha S. Feldman

University of Michigan at Ann Arbor - Gerald R. Ford School of Public Policy ( email )

735 South State Street, Weill Hall
Ann Arbor, MI 48109
United States

Markus C. Becker

University of Southern Denmark ( email )

Campusvej 55
Odense, 5000
Denmark

Peng Liu

affiliation not provided to SSRN

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