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The firms that feed industrial districts: A return to the Italian source
Abstract
Italian industrial districts comprise a great variety of firms and are criss-crossed by various networks and business groups. Although the vast majority of industrial district firms are relatively small, it is a mistake to assume that they are organizationally symmetrical and that they mimic the supposedly dominant norms of the communities in which they are located. Larger firms frequently organize production among groups of smaller firms, introduce technological innovations and expand existing markets.
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Citations
Cited by
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Inkpen, A. C., & Tsang, E. W. K. (2005) · Academy of Management Review · doi:10.5465/amr.2005.15281445 · Cited by 3,864
Deconstructing clusters: chaotic concept or policy panacea?
Martin, R., & Sunley, P. (2003) · Journal of Economic Geography · doi:10.1093/jeg/3.1.5 · Cited by 2,234
Knowledge Spillovers and Local Innovation Systems: A Critical Survey
Breschi, S. (2001) · Industrial and Corporate Change · doi:10.1093/icc/10.4.975 · Cited by 1,536
Boschma, R. A., & ter Wal, A. L. J. (2007) · Industry & Innovation · doi:10.1080/13662710701253441 · Cited by 657
Gatekeepers of Knowledge within Industrial Districts: Who They Are, How They Interact
Morrison, A. (2008) · Regional Studies · doi:10.1080/00343400701654178 · Cited by 441
Metadata
- Authors
- Lazerson, M.
- Venue
- Industrial and Corporate Change
- Publisher
- Oxford University Press (OUP)
- Published
- 1999-06-01
- Volume
- 8
- Issue
- 2
- Pages
- 235-266
- ISSN
- 0960-6491 (ISSN-L)
- 1464-3650
- OpenAlex
- W2078675616
- Cited by
- 398
- References
- 0
- FWCI
- 36.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lazerson, M. (1999). The firms that feed industrial districts: A return to the Italian source. Industrial and Corporate Change. https://doi.org/10.1093/icc/8.2.235
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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.
