324
Views
0
CrossRef citations to date
0
Altmetric
Article

Sustainable competitiveness in the relational production network of London’s Savile Row tailors

&
Pages 370-380 | Received 25 Sep 2017, Accepted 14 Jun 2019, Published online: 03 Jul 2019
 

Abstract

Slow production methods are gaining ground based on the concept of slow fashion. Slowness assumes that specialised clothing production remains local, and consumption is centred on the superior quality which often involves craft production. Competitiveness represents a major challenge in slow manufacture, as highly specialised clothing companies may need to be viable within the high salaries labour ecosystem of developed economies. The study proposes a model based on the theories of relational production networks, coopetition and sustainable competitiveness in urbanisation economies. It attempts to explore empirically how slowness in high-end clothing production can be competitive. A single case study is adopted to investigate the production network of the Savile Row tailors, where tailoring firms have demonstrated a remarkable resilience for nearly two centuries. It appears that in the flat and self-reliant tailoring network, the participant firms have managed to acquire capabilities and specialised knowledge, and transformed them into core competences, thus generating sustainable competitiveness.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Ministry of Science of Technology in Taiwan (No. MOST 105-2410-H-364-004).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 268.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.