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Cited by 282

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

An empirical evaluation of the internal corporate venturing process

Garud, Raghu, van de ven, Andrew H.·1992·Strategic Management Journal

Abstract

This paper develops a model of the internal corporate venturing process. The model explores conditions under which entrepreneurs are likely to continue with a course of action despite experiencing negative outcomes. Persisting with a course of action despite associated negative outcomes runs counter to trial-and-error learning behavior. We suggest that entrepreneurs are likely to continue with a course of action despite experiencing negative outcomes when the level of ambiguity is high and slack resources are available. In contrast, trial-and-error learning is likely to occur when either the level of ambiguity is low or when slack resources are not available. We examine these and other aspects of the venturing process with longitudinal data on the development of one venture. The data were collected over a 12-year period during the commercial development of cochlear implants within a large diversified corporation. The results support our core hypotheses on trial-and-error learning and action persistence while extending our understanding of associated processes.

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Crossref · 10.1002/smj.4250131008

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Metadata

Authors
Garud, R., & van de ven, A. H.
Venue
Strategic Management Journal
Publisher
Wiley
Published
1992
Volume
13
Issue
S1
Pages
93-109
ISSN
  • 0143-2095 (ISSN-L)
  • 1097-0266
OpenAlex
W2051274604
Cited by
282
References
4
FWCI
5.58
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An empirical evaluation of the internal corporate venturing process. https://app.cochat.ai/papers/10.1002/smj.4250131008
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Garud, R., & van de ven, A. H. (1992). An empirical evaluation of the internal corporate venturing process. Strategic Management Journal. https://doi.org/10.1002/smj.4250131008

Data sources

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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.