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Cited by 253

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

HUMAN RESOURCES AS STRATEGIC ASSETS: AN EVOLUTIONARY RESOURCE‐BASED THEORY*

Mueller, Frank·1996·Journal of Management Studies

Abstract

The paper proposes an evolutionary approach to strategic human resources. This means that, first of all, truly valuable strategic assets are unlikely to result directly from senior management policies. Rather, what is truly valuable is the ‘social architecture’ that results from ongoing skill formation activities, forms of spontaneous co‐operation, the tacit knowledge that accumulates as the unplanned side‐effect of intentional corporate behaviour. Thus, corporate prosperity not seldom rests in the social architecture that has emerged slowly and incrementally over time, and may even predate the tenure of current senior management. Given the low visibility of such spontaneous co‐operation, it is even more likely to be resistant to easy imitation and therefore a valuable strategic asset.

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Integrity signals

Registry

Crossref · 10.1111/j.1467-6486.1996.tb00171.x

Cites

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Metadata

Authors
Mueller, F.
Venue
Journal of Management Studies
Publisher
Wiley
Published
1996-11
Volume
33
Issue
6
Pages
757-785
ISSN
  • 0022-2380 (ISSN-L)
  • 1467-6486
OpenAlex
W2035129144
Cited by
253
References
5
FWCI
11.75
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: HUMAN RESOURCES AS STRATEGIC ASSETS: AN EVOLUTIONARY RESOURCE‐BASED THEORY*. https://app.cochat.ai/papers/10.1111/j.1467-6486.1996.tb00171.x
The paper
Mueller, F. (1996). HUMAN RESOURCES AS STRATEGIC ASSETS: AN EVOLUTIONARY RESOURCE‐BASED THEORY*. Journal of Management Studies. https://doi.org/10.1111/j.1467-6486.1996.tb00171.x

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.