Efficiency evaluation and loan assessment of fashion upcyclers in Liberia using fuzzy, DEA and FIS models

Abstract

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.

An assessment of Africa’s second-hand clothing value chain: a systematic review and research opportunities

“…On the other hand, one study Sumo et al is based on decision-support modeling using multi-criteria decision-making (MCDM) approaches and data envelopment analysis (DEA). 27 Our result shows that many authors choose to conduct exploratory and desk studies rather than fresh empirical studies. This is highly associated with the limited data on SHC in Africa, as much essential information about the used clothing trade goes unreported in international trade statistics.…”

“…18 SHC offers African societies many significant benefits, particularly in clothing recycling, 29 job creation, 15,16,20 and responsible consumerism. 27,40,41 The formalization of the SHC trade in countries such as Malawi is not only helping to provide affordable clothing to poor people but is also resulting in the establishment of a more effective organizational structure and relationships between different sales channels that are fueling the growth of small businesses. 16 A similar SHC trade formalization initiative plays a crucial role in Ethiopia's consumption and retail segment since the sector sustains livelihoods in the country.…”

“…SHC is a dominant feature with significant consumer benefits as it supports livelihoods and provides employment. 13,20,27 It serves as a quick way of creating jobs and sustaining the livelihoods of the teeming unemployed youthful population in Africa. 20,27,40 Its employment generation in the value chain is enormous.…”

What drives the profitability of Indian handloom enterprises? An insight based on the seemingly unrelated regression model

“…The current strand of research sheds light on various issues affecting the microenterprises, including determinants of modern technology adoption (Sumo et al, 2022;Ramachandran et al, 2012;Bortamuly et al, 2015;Hazarika et al, 2016), determinants of occupational choice (Bortamuly et al, 2013), handloom market development (Singh et al, 2015), wage structure of handloom workers (Bortamuly and Goswami, 2012), handloom supply chain (Panigrahi and Rao, 2018;Mahapatra et al, 2019), financial risk attitude among handloom entrepreneurs (Goswami et al, 2017), women empowerment (Bortamuly et al, 2015;Hazarika, 2018;Singha and Singha, 2020), life cycle assessment of handloom products (Bhalla et al, 2018), marketing opportunities of geographical indication tagged handloom products (Verma and Mishra, 2018), antecedents of purchase of handloom products (Agostino et al, 2020), the contribution of handloom in sustainable development goals (Bhowmik, 2021). However, no study has focused on the profitability of the Indian handloom sector.…”

Are handloom micro-enterprises in India efficient? Estimation based on DEA and bootstrap truncated regression approach

“…In contrast to SFA, DEA is a non-parametric approach that makes no assumptions about the underlying technology and assumes no functional form of the production function (Ray, 2004; Singh and Gundimeda, 2021). The use of DEA in efficiency analysis of the manufacturing sector has increased dramatically because of its flexibility and less restrictive nature (Kotey and O’Donnell, 2002; Joshi and Singh, 2011; Kostov et al , 2019; Nomani and Sen, 2019; Adams et al , 2020; Dalei and Joshi, 2020; Sumo et al , 2022). Apart from these, few studies applied DEA second-stage regression with the Tobit model to identify the determinants of technical efficiency in such studies (Joshi and Singh, 2011; Vixathep and Matsunaga, 2012a, 2012b; Abdulla and Kumar, 2021).…”