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Cited by 491

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Ugly Duckling No More: Pasts and Futures of Organizational Learning Research

Miner, Anne S., Mezias, Stephen J.·1996·Organization Science

Abstract

This article addresses theoretical and research frontiers for learning research, a second theme of Professor Argyris essay—the lead article in the “Crossroads” section. We outline three key theoretical questions for further work. We call for more systematic empirical learning research, suggesting that the paucity of such research may have resulted less from defensiveness than from the demanding requirements of doing crisp, systematic learning research. The need for scholarly empirical work is enhanced, we believe, by the growing popularity of organizational learning models among practitioners. Concurring with Professor Argyris' broad concern with enhancing research fruitfulness, we suggest ways to supplement traditional organizational research methods. In particular, we argue that it makes sense to cast wider nets for models of learning and adaptation, to sustain qualitative investigation, to use simulation techniques, and to maintain stronger—and perhaps even experimental—linkages between applied and theoretical research.

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Metadata

Authors
Miner, A. S., & Mezias, S. J.
Venue
Organization Science
Published
1996-02
Volume
7
Issue
1
Pages
88-99
ISSN
  • 1047-7039 (ISSN-L)
  • 1526-5455
OpenAlex
W2097138969
Cited by
491
References
3
FWCI
19.58
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Ugly Duckling No More: Pasts and Futures of Organizational Learning Research. https://app.cochat.ai/papers/10.1287/orsc.7.1.88
The paper
Miner, A. S., & Mezias, S. J. (1996). Ugly Duckling No More: Pasts and Futures of Organizational Learning Research. Organization Science. https://doi.org/10.1287/orsc.7.1.88

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.