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Lean enough: Institutional logics of best practice and managerial satisficing in American manufacturing

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journal contribution
posted on 24.11.2017, 14:24 by Matt Vidal
Rational choice theory has been widely criticized for its unrealistic assumptions that individuals have perfect information and computer-like information processing capability, which are used to maximize utility. Sociological institutionalism and the behavioral theory of the firm have developed complementary alternatives. I combine the two into a single model of information processing. Institutional logics are central to top-down (schema-driven) processes that focus attention and guide action. Satisficing—settling for good enough based on a given aspiration level—is critical to bottomup (feedback-driven) information processing. Here I show that two practices associated with the postfordist logic of the capitalist firm—lean production and worker empowerment—are deeply institutionalized as best practice in the American manufacturing field. Based on interviews with 109 individuals in 31 firms, I demonstrate how moderate aspiration levels and conceptual schemas associated with formerly dominant fordist institutional logics both function to limit the adoption of best practice.

Funding

The research for this paper was made possible by a grant from the Alfred P. Sloan Foundation, with further support provided by the UW Center on Wisconsin Strategy (COWS) and the UCLA Institute for Research on Labor and Employment (IRLE).

History

School

  • Loughborough University London

Published in

Socius: Sociological Research for a Dynamic World

Volume

3

Pages

237802311773694 - 237802311773694

Citation

VIDAL, M., 2017. Lean enough: Institutional logics of best practice and managerial satisficing in American manufacturing. Socius: Sociological Research for a Dynamic World, 3, n.p.

Publisher

© The authors. Published by SAGE Journals

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by-nc/4.0/

Acceptance date

25/08/2017

Publication date

2017-11-06

Copyright date

2017

Notes

This is an Open Access Article. It is published by Sage under the Creative Commons Attribution 4.0 Unported Licence (CC BY-NC). Full details of this licence are available at: http://creativecommons.org/licenses/by-nc/4.0/

ISSN

2378-0231

eISSN

2378-0231

Language

en

Exports