NDLI: The Stability of Self-Organized Rule-Following Work Teams

Content Provider	Springer Nature Link
Author	Walker, Crayton C. Dooley, Kevin J.
Copyright Year	1999
Abstract	Self-organized rule-following systems are increasingly relevant objects of study in organization theory due to such systems&2018; capacity to maintain control while enabling decentralization of authority. This paper proposes a network model for such systems and examines the stability of the networks&2018; repetitive behavior. The networks examined are Ashby nets, a fundamental class of binary systems: connected aggregates of nodes that individually compute an interaction rule, a binary function of their three inputs. The nodes, which we interpret as workers in a work team, have two network inputs and one self-input. All workers in a given team follow the same interaction rule.We operationalize the notion of stability of the team&2018;s work routine and determine stability under small perturbations for all possible rules these teams can follow. To study the organizational concomitants of stability, we characterize the rules by their memory, fluency, homogeneity, and autonomy. We relate these measures to work routine stability, and find that stability in ten member teams is enhanced by rules that have low memory, high homogeneity, and low autonomy.
Starting Page	5
Ending Page	30
Page Count	26
File Format	PDF
ISSN	1381298X
Journal	Computational and Mathematical Organization Theory
Volume Number	5
Issue Number	1
e-ISSN	15729346
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	1999-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial Intelligence (incl. Robotics) Management Operation Research/Decision Theory Methodology of the Social Sciences Sociology
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Computer Science Decision Sciences Computational Mathematics Modeling and Simulation

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