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Cited by 1,258

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

Learning from Samples of One or Fewer

March, James G., Sproull, Lee S., Tamuz, Michal·1991·Organization Science

Abstract

Organizations learn from experience. Sometimes, however, history is not generous with experience. We explore how organizations convert infrequent events into interpretations of history, and how they balance the need to achieve agreement on interpretations with the need to interpret history correctly. We ask what methods are used, what problems are involved, and what improvements might be made. Although the methods we observe are not guaranteed to lead to consistent agreement on interpretations, valid knowledge, improved organizational performance, or organizational survival, they provide possible insights into the possibilities for and problems of learning from fragments of history.

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Metadata

Authors
March, J. G., Sproull, L. S., & Tamuz, M.
Venue
Organization Science
Published
1991-02
Volume
2
Issue
1
Pages
1-13
ISSN
  • 1047-7039 (ISSN-L)
  • 1526-5455
OpenAlex
W2001606006
Cited by
1,258
References
4
FWCI
21.68
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Learning from Samples of One or Fewer. https://app.cochat.ai/papers/10.1287/orsc.2.1.1
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March, J. G., Sproull, L. S., & Tamuz, M. (1991). Learning from Samples of One or Fewer. Organization Science. https://doi.org/10.1287/orsc.2.1.1

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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.