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Cited by 366

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

Populations, Natural Selection, and Applied Organizational Science

McKelvey, Bill, Aldrich, Howard·1983·Administrative Science Quarterly

Abstract

? 1983 by Cornell University 0001-8392/83/2801-01 01 /$00 75 This paper proposes that organizational science could be applied more widely if the field were more concerned with the conditions under which research findings are valid. Papers in the field generalize about organizations as if they were all alike, or refrain from generalizing at all, as if they were all unique. The population perspective presented de-emphasizes the all-alike and all-unique approaches, placing emphasis instead on research methods that improve the description and classification of organizational forms, define more homogeneous groupings, and specify the limited conditions under which predictions may be expected to hold true. The principles of the population perspective are reviewed, and an outline is presented for developing a classification of organizational forms. Suggestions are then made on how to use the perspective to increase and improve the application of organizational research.

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Metadata

Authors
McKelvey, B., & Aldrich, H.
Venue
Administrative Science Quarterly
Publisher
JSTOR
Published
1983-03
Volume
28
Issue
1
Pages
101
ISSN
0001-8392 (ISSN-L)
OpenAlex
W2026636135
Cited by
366
References
40
FWCI
19.23
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Populations, Natural Selection, and Applied Organizational Science. https://app.cochat.ai/papers/10.2307/2392389
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McKelvey, B., & Aldrich, H. (1983). Populations, Natural Selection, and Applied Organizational Science. Administrative Science Quarterly. https://doi.org/10.2307/2392389

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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.