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Cited by 297

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

An Empirical Investigation of the Microstructure of Knowledge Acquisition and Transfer Through Learning by Doing

Epple, Dennis, Argote, Linda, Murphy, Kenneth·1996·Operations Research

Abstract

Does knowledge acquired through learning by doing on one shift transfer to a second shift when it is introduced at a manufacturing plant? The answer to this question has important theoretical implications about where knowledge is embedded in organizations and about sources of productivity growth. The answer also has important practical implications for managers planning to introduce additional facilities. This paper analyzes the amount of transfer across shifts at a manufacturing facility. Specifically, we analyze the amount of knowledge that is carried forward when the plant makes the transition from one to two shifts. We also investigate whether the rate of knowledge acquisition differs by shift, and we estimate the amount of transfer that occurs across shifts once both are in operation. In addition, we study transfer over time by analyzing whether knowledge acquired through learning by doing is cumulative and persists through time or whether it depreciates.

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Metadata

Authors
Epple, D., Argote, L., & Murphy, K.
Venue
Operations Research
Published
1996-02
Volume
44
Issue
1
Pages
77-86
ISSN
  • 0030-364X (ISSN-L)
  • 1526-5463
OpenAlex
W2112034264
Cited by
297
References
1
FWCI
5.62
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An Empirical Investigation of the Microstructure of Knowledge Acquisition and Transfer Through Learning by Doing. https://app.cochat.ai/papers/10.1287/opre.44.1.77
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Epple, D., Argote, L., & Murphy, K. (1996). An Empirical Investigation of the Microstructure of Knowledge Acquisition and Transfer Through Learning by Doing. Operations Research. https://doi.org/10.1287/opre.44.1.77

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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.