JOURNAL ARTICLE

Giving fast fashion the boot? Producing, consuming and branding slow(er) fashion in luxury shoe manufacturing in Northamptonshire.

  • Published In: International Journal of Sustainable Fashion & Textiles, 2025, v. 4, n. 1. P. 37 1 of 3

  • Database: Textile Technology Complete 2 of 3

  • Authored By: Phelan, Kieran 3 of 3

Abstract

This article critically examines the concept of slow fashion through an empirical case study of the luxury shoe manufacturing cluster in Northamptonshire, UK. It distinguishes between "slow fashion"—defined as a theory, ethic, and practice aimed at systemic change—and "slow(er) fashion," which adopts slow principles superficially for commercial advantage without an explicit political commitment to transforming fashion systems. The study finds that Northamptonshire shoemakers’ slow production methods, rooted in traditional Goodyear-welted craftsmanship, are often framed as slow by default rather than by intentional design, and their branding leverages slow narratives to enhance product value and consumer connection. While these brands promote notions of responsible consumption and repairability, such claims rest on assumptions about affluent consumers and may obscure ongoing commercial priorities and complex production geographies, including outsourcing. The article calls for greater critical scrutiny of slow fashion branding to avoid "slow washing" and to preserve slow fashion’s potential as a transformative alternative within the fashion industry.

Additional Information

  • Source:International Journal of Sustainable Fashion & Textiles. 2025/05, Vol. 4, Issue 1, p37
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2025
  • ISSN:2754-026X
  • DOI:10.1386/sft_00056_1
  • Accession Number:187011661
  • Copyright Statement:Copyright of International Journal of Sustainable Fashion & Textiles is the property of Intellect Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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