Purpose
Studies on textile upcycling in Africa are rare, particularly in Liberia, where extensive upcycling designs are appreciated throughout the country. This study aims to contribute to the upcycling literature from the perspective of Liberia’s fashion upcyclers by assessing their coping strategies and understanding the challenges confronting fashion upcycling in Monrovia’s four largest markets.
Design/methodology/approach
A fuzzy analytical hierarchy process and data envelopment analysis (DEA) models were used to assess labor input, delivery and flexibility, technological and innovation capability, financial capability, pricing of finished products, customer service and quality outputs of upcycled fashions. The fuzzy inference system model assessed upcyclers’ loaning eligibility.
Findings
The results highlight that Liberia’s fashion upcycling is expanding with varying innovative designs. The quality of upcycled fashions was deemed most important in the proposed AHP model. However, many upcycling businesses lack sufficient capital to make long-term investments. With the necessary investment, the innovation of these upcyclers could be a new line of fashion brands with great potential. In addition, using a fair judgment in assessing the little loaning funds available is paramount to enhancing their growth.
Research limitations/implications
Only 34 decision-making units were assessed. Future research could expand this scope using other models with more practical loaning strategies.
Originality/value
This study presents a wealth of managerial and policy implications. The proposed hybrid model is adequate for developing managerial decisions for fashion upcyclers. The proposed framework can manage ambiguity, inaccuracy and the complexity of making decisions based on numerous criteria, making it applicable in unearthing robust strategies for enhancing the fashion upcycling sectors and other industries in developing countries. In addition, the proposed fuzzy Mamdani system could also be extended to other sectors, such as agriculture, for a more transparent allocation of resources.