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Innovation Speed: A Conceptual Model of Context, Antecedents, and Outcomes
Abstract
There is a growing recognition that innovation speed is important to a firm's creating and sustaining competitive advantage amidst rapidly changing business environments. However, there has been little theoretical advancement or model building regarding when innovation speed is appropriate, what factors speed up innovations, and how differences in speed affect project outcomes. In this article, we organize and integrate the innovation speed literature, develop a conceptual framework of innovation speed, and offer researchable propositions relating to the need for and antecedents and outcomes of innovation speed. Specifically, we argue that innovation speed (a) is most appropriate in environments characterized by competitive intensity, technological and market dynamism, and low regulatory restrictiveness; (b) can be positively or negatively affected by strategic-orientation factors and organizational-capability factors; and (c) has an influence on development costs, product quality, and ultimately project ...
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Metadata
- Authors
- Kessler, E. H., & Chakrabarti, A. K.
- Venue
- The Academy of Management Review
- Publisher
- Academy of Management
- Published
- 1996-10
- Volume
- 21
- Issue
- 4
- Pages
- 1143
- ISSN
- 0363-7425 (ISSN-L)
- OpenAlex
- W2121391934
- Cited by
- 847
- References
- 172
- FWCI
- 22.03
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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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.