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Cited by 417

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

Learning While Innovating

Van de Ven, Andrew H., Polley, Douglas·1992·Organization Science

Abstract

This paper examines processes of trial-and-error learning during the development of a technological innovation by an interorganizational joint venture created expressly for developing and commercializing products from the new technology. We develop a model of adaptive learning, which incorporates elements from laboratory models of learning and applies them to the field research setting. The learning model focuses on relationships between the goals, actions, and outcomes of an innovation team within the joint venture as it develops the innovation over time, and the influences that environmental events and external interventions by resource controllers in parent companies have on the learning process. The model is tested based on a real-time longitudinal study of the development of a biomedical innovation (therapeutic apheresis) from 1983 to 1988. Different patterns of learning were observed in different periods of innovation development. Event time series analyses clearly contradict the learning model during an initial expansion period, but strongly support the model during a subsequent contraction period. Explanations for why these different patterns of organizational learning occurred over time are provided, and focus on a set of organizational structures and practices which are commonly used to manage innovation development, but which inhibit learning.

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Crossref · 10.1287/orsc.3.1.92

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Metadata

Authors
Van de Ven, A. H., & Polley, D.
Venue
Organization Science
Published
1992-02
Volume
3
Issue
1
Pages
92-116
ISSN
  • 1047-7039 (ISSN-L)
  • 1526-5455
OpenAlex
W2114846808
Cited by
417
References
3
FWCI
29.15
Type
journal article
Language
en
Open access
closed

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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.