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Cited by 406

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Organizational Learning and Personnel Turnover

Carley, Kathleen·1992·Organization Science

Abstract

The impact of personnel turnover on an organization's ability to learn, and hence on its ultimate performance, is explored for organizations with different structures and different tasks. A model of organizational decision making is presented where: (1) the organization is faced with a continuous sequence of similar but not identical problems; (2) each problem is so complex that no one person has access to all of the information nor the skill to comprehend all of the information necessary to make the decision; (3) individual decision makers base their decisions on their own previous experience; and (4) there is personnel turnover. Using simulation the impact of turnover on the rate and level of learning for hierarchies and teams is examined. This research suggests that while teams in general learn faster and better than hierarchies, hierarchies are less affected by high turnover rates particularly when the task is nondecomposable. Institutionalized memory, as embodied in the memories of distributed individuals and in the advisory relationships between individuals, determines the consequences of personnel turnover.

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Metadata

Authors
Carley, K.
Venue
Organization Science
Published
1992-02
Volume
3
Issue
1
Pages
20-46
ISSN
  • 1047-7039 (ISSN-L)
  • 1526-5455
OpenAlex
W1987332679
Cited by
406
References
4
FWCI
16.91
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Organizational Learning and Personnel Turnover. https://app.cochat.ai/papers/10.1287/orsc.3.1.20
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Carley, K. (1992). Organizational Learning and Personnel Turnover. Organization Science. https://doi.org/10.1287/orsc.3.1.20

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.