Analysing the barriers to green apparel manufacturing implementation

• Published in: Journal of fashion marketing and management 2023-05, Vol.27 (3), p.531-560
• Main Author: Guo, Hailan
• Format: Article
• Language: English
• Subjects: Clothing industry; Communication; Developing countries; Landfill; LDCs; Literature reviews; Manufacturing; Operating costs; Politics; Supply chains
• ISSN: 1361-2026; 1758-7433; 1361-2026
• DOI: 10.1108/JFMM-09-2021-0226

PurposeApparel manufacturers' achievement of green manufacturing (GM) goal remains low. This paper aims to identify and prioritise the barriers to GM implementation in apparel companies.Design/methodology/approachFirst, an extensive literature review is conducted to identify the key barriers to GM implementation. Second, 374 usable questionnaires are collected from apparel manufacturing companies to (a) examine the impact of and (b) rank the barriers. Third, interpretive structural modelling (ISM) is applied to test the relationships among barriers. Finally, structural equation modelling (SEM) is applied to improve the model derived from the ISM.FindingsThe results reveal that the independent barriers – lack of eco-literacy among upstream or downstream supply chain members, lack of specific company-level training and monitoring of the progress of GM implementation and inadequate support from regulatory authorities – are the root causes of all the barriers. These three barriers are also at a low level of the ISM model, indicating that they significantly affect the entire system and therefore should be accorded the highest priority in dealing with these barriers.Practical implicationsThe results are useful for decision-makers and apparel companies to understand identified barriers, develop potential GM interventions and formulate appropriate strategies to overcome these barriers.Originality/valueThe listed barriers are yet to be comprehensively synthesised in a coherent model and empirically tested in the apparel sector using a combination of the ISM and SEM techniques. The empirically validated model presented in this study is an important step in that direction.