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Cited by 1,457

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Process Management and Technological Innovation: A Longitudinal Study of the Photography and Paint Industries

Benner, Mary J., Tushman, Michael·2002·Administrative Science Quarterly
DOI 10.2307/3094913
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Abstract

This research explores the impact of process management activities on technological innovation. Drawing on research in organizational evolution and learning, we suggest that as these practices reduce variance in organizational routines and influence the selection of innovations, they enhance incremental innovation at the expense of exploratory innovation. We tested our hypotheses in a 20-year longitudinal study of patenting activity and ISO 9000 quality program certifications in the paint and photography industries. In both industries, the extent of process management activities in a firm was associated with an increase in both exploitative innovations that built on existing firm knowledge and an increase in exploitation's share of total innovations. Our results suggest that exploitation crowds out exploration. We extend existing empirical research by capturing how process management activities influence the extent to which innovations build on existing firm knowledge. We suggest that these widely adopted organizational practices shift the balance of exploitation and exploration by focusing on efficiency, possibly at the expense of long-term adaptation.

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Metadata

Authors
Benner, M. J., & Tushman, M.
Venue
Administrative Science Quarterly
Publisher
JSTOR
Published
2002-12
Volume
47
Issue
4
Pages
676-707
ISSN
  • 0001-8392 (ISSN-L)
  • 1930-3815
DOI
10.2307/3094913
OpenAlex
W2075412397
Cited by
1,457
References
7
FWCI
41.98
Type
journal article
Language
en
Open access
closed

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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.