Submitted:
04 October 2023
Posted:
06 October 2023
Read the latest preprint version here
Abstract
Keywords:
1. Introduction
2. Methodology
2.1. Identified Studies
2.2. Screened Studies
2.3. Analyses of Studies and Bias
3. Results and Discussion
- -
- Low number of participants/objects under study, limited geographies included (namely one country or in the same region), and limited range of time of the participation (Padilha and Gomes, 2016; Camacho-Otero et al., 2019; Brydges, 2021; Dhir et al., 2021; Fontana et al., 2021; Gossen and Heinrich, 2021; Sandberg and Hultberg, 2021; Siderius et al., 2021; Turunen and Halme, 2021; Bocken and Konietzko, 2022; Bhandari et al., 2022; Dangelico et al., 2022; de Oliveira et al., 2022; Fuchs and Hovemann, 2022; Gossen and Kropfeld, 2022; Louma et al., 2022; Nayak et al., 2022; Salmi and Kaipia, 2022; Valor et al., 2022; Xue et al., 2022; Aydin et al., 2023; Garcia-Ortega et al., 2023; Kautish et al., 2023; Román-Collado et al., 2023; Pandey and Yadav, 2023; Scott et al., 2023; Shamsuzzaman et al., 2023).
- -
- Limited knowledge and few technological tools from and for the stakeholders to enhance sustainability and/or collaborative actions (Repp et al., 2021; Pal et al., 2021; Siderius et al., 2021; Cooper and Claxton, 2022; Bhandari et al., 2022; Dragomir and Dumitru, 2022; Fuchs and Hovemann, 2022; Gossen and Kropfeld, 2022; Laukkanen and Tura, 2022; Louma et al., 2022; Nayak et al., 2022; Shou et al., 2022; Valor et al., 2022; Kautish et al., 2023; López et al., 2023; Millward-Hopkins et al., 2023; Román-Collado et al., 2023)
- -
- Limited scope of the developed methodology (Padilha and Gomes, 2016; Dhir et al., 2021; Repp et al., 2021; Martin and Herlaar, 2021; Turunen and Halme, 2021; Angelis-Dimakis et al., 2022; Bhandari et al., 2022; Bocken and Konietzko, 2022; Dragomir and Dumitru, 2022; Fuchs and Hovemann, 2022; Laukkanen and Tura, 2022; Mölsä et al., 2022; Schmutz and Som, 2022; Shou et al., 2022; Valor et al., 2022; Aydin et al., 2023; Kautish et al., 2023; López et al., 2023; Millward-Hopkins et al., 2023; Sandberg, 2023; Scott et al., 2023; Shamsuzzaman et al., 2023)
3.1. Discussion using ChatGPT1
3.1.1. First Round
- (1)
- Material Selection
- (2)
- Supply Chain Transparency
- (3)
- Waste Management
- (4)
- Consumer Behavior
- (5)
- Collaboration and Industry Transformation
- (6)
- Scaling Sustainable Solutions
- (7)
- Longevity and Timelessness
- (1)
- Problem Identification and Research Question
- -
- Clearly define the problem or challenge related to sustainable fashion design.
- -
- Formulate a research question that guides your investigation.
- (2)
- Literature Review
- -
- Conduct a comprehensive review of existing literature and research related to sustainable fashion design, materials, supply chains, waste management, consumer behaviour, collaboration, and industry transformation.
- -
- Identify gaps and areas that require further exploration.
- (3)
- Hypothesis Generation
- -
- Based on the literature review, generate hypotheses or potential solutions that address the identified challenges.
- -
- These hypotheses should propose innovative and sustainable approaches to the problem.
- (4)
- Data Collection
- -
- Design and implement data collection methods that align with your research question and hypotheses.
- -
- This may involve qualitative methods such as interviews, focus groups, and observations, as well as quantitative methods like surveys and data analysis.
- (5)
- Idea Generation and Ideation
- -
- Utilize creative methods and ideation techniques, such as brainstorming sessions, design thinking workshops, or participatory design, to generate a wide range of potential solutions.
- -
- Encourage diverse perspectives and collaboration among stakeholders to foster creativity.
- (6)
- Prototyping and Testing
- -
- Select a subset of the generated ideas and develop prototypes or minimum viable products (MVPs) that represent the potential solutions.
- -
- Test the prototypes in controlled environments or real-life scenarios to assess their feasibility, functionality, and sustainability performance.
- -
- Gather feedback from users, experts, and other relevant stakeholders to refine the prototypes.
- (7)
- Evaluation and Analysis
- -
- Analyse the data collected from testing and prototype evaluation.
- -
- Assess the potential solutions based on predefined criteria, such as environmental impact, social implications, economic viability, scalability, and compatibility with industry practices.
- -
- Use statistical analysis, qualitative analysis, or a combination of both to draw meaningful conclusions.
- (8)
- Iteration and Refinement
- -
- Based on the evaluation results, refine and iterate on the potential solutions.
- -
- Modify the prototypes or generate new ideas if necessary.
- -
- Repeat the testing and evaluation process to continuously improve and validate the feasibility of the solutions.
- (9)
- Documentation and Reporting
- -
- Document the research methodology, data collection process, results, and conclusions.
- -
- Communicate findings through research papers, reports, presentations, or other appropriate mediums.
- -
- Share insights with the academic community, industry professionals, policymakers, and other relevant stakeholders.
3.1.2. Second round
- -
- Assemble a multidisciplinary research team consisting of experts in sustainable fashion, design, supply chain management, consumer behavior, and technology.
- -
- Conduct an extensive literature review to gather existing knowledge on sustainable fashion practices, methodologies, and frameworks.
- -
- Identify and engage with key stakeholders in the fashion industry, including designers, manufacturers, policymakers, and NGOs, to understand their challenges, needs, and perspectives.
- -
- Design and implement a large-scale global research study that overcomes the handicaps of current methodologies:
- -
- Ensure a diverse and representative participant pool from various countries and regions, involving designers, professionals, and other stakeholders from different fashion sectors.
- -
- Employ advanced technology tools, such as data analytics, machine learning, and blockchain, to enhance sustainability and collaborative actions within the study.
- -
- Develop an expansive and adaptable methodology that encompasses different geographies, cultures, and timeframes to capture a comprehensive understanding of sustainable fashion practices.
- -
- Collect data through mixed methods, including surveys, interviews, observations, and industry case studies, to generate rich insights into sustainable fashion practices, challenges, and opportunities.
- -
- Apply iterative and participatory design processes, involving stakeholders in co-creation sessions to refine and validate the framework's components.
- -
- Continuously assess the feasibility and practicality of the framework to ensure its actionability and adaptability across the fashion industry.
- -
- Validate the framework through pilot implementations in collaboration with selected fashion companies and organizations.
- -
- Assess the impact of implementing the framework on sustainability performance, consumer behavior, supply chain transparency, and collaboration within the participating organizations.
- -
- Gather feedback from practitioners and stakeholders involved in the pilot implementations to refine and optimize the framework.
- -
- Develop practical guidelines, toolkits, and training materials that distill the framework's key principles and actionable steps for easy adoption across the fashion industry.
- -
- Disseminate the research findings, framework, and associated resources through publications, industry conferences, workshops, and online platforms.
- -
- Collaborate with global fashion associations, NGOs, and policymakers to advocate for the adoption of the framework and support its implementation at a broader scale.
4. Conclusions
Conflicts of Interest
Copyright Consent
Declaration of Generative AI and AI-assisted technologies in the writing process
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| 1 | ChatGPT, personal communication, May 26-30, 2023) |
| Identification Criterion | Description |
|---|---|
| Research objective | Investigate the impact of fashion research methodologies in sustainable design practice |
| Database | Scopus, assessed in April 15th 2023 |
| Article types | Research articles, Case reports |
| Keywords | Fashion research, sustainability, methods, methodology, methodologies, limitations, challenges, fashion industry, environmental impact, fashion research methods |
| Language | English |
| Research criteria | Based on 3 research criteria: (1) ‘fashion research’ and ‘fashion design’ and ‘sustainability’, (2) ‘fashion research and ‘fashion design’ and ‘sustainability’ and ‘methods’ or ‘methodology’ or ‘methodologies’ and limited to the subject areas ‘Business, Management and Accounting’ and ‘Environmental Science’, (3) ‘fashion research and ‘fashion design’ and ‘sustainability’ and ‘methods’ or ‘methodology’ or ‘methodologies’ and ‘limitations’ or ‘challenges’ and ‘fashion industry and ‘environmental impact’ and ‘fashion research methods’ and limited to the same subject areas. |
| Research years | 2015–2023 |
| Type of Method (n = total) | Reference |
|---|---|
| Adaptive choice-Based conjoint analysis (n = 1) | Fuchs and Hovemann, 2022 |
| Case study (n = 10) | Camacho-Otero et al., 2019; Brydges, 2021; Pal et al., 2021; Cooper and Claxton, 2022; Dragomir and Dumitru, 2022; Gossen and Kropfeld, 2022; Salmi and Kaipia, 2022; Valor et al., 2022; Garcia-Ortega et al., 2023; López et al., 2023 |
| Compositional analysis (n = 1) | Wesley et al., 2023 |
| Focus group (n = 1) | Aydin et al., 2023 |
| Framework development/ Theoretical model (n = 5) | Turunen and Halme, 2021; Shou et al., 2022; Aydin et al., 2023; Kautish et al., 2023; Shamsuzzaman et al., 2023 |
| Index Decomposition Analysis through the Logarithmic mean divisia index, and decoupling analysis (n = 1) | Román-Collado et al., 2023 |
| Interactive action research (n = 1) | Sandberg, 2023 |
| Interview (n = 13) | Padilha and Gomes, 2016; Singh et al., 2019; Fontana et al., 2021; Gossen and Heinrich, 2021; Sandberg and Hultberg, 2021; Siderius et al., 2021; Bocken and Konietzko, 2022; Laukkanen and Tura, 2022; Nayak et al., 2022; Amasawa et al., 2023; Dukovska-Poposka et al., 2023; Persson and Hinton, 2023; Shamsuzzaman et al., 2023 |
| Life cycle assessment (n = 7) | Stone et al., 2020; Martin and Herlaar, 2021; Angelis-Dimakis et al., 2022; Mölsä et al., 2022; Shou et al., 2022; Amasawa et al., 2023; Wesley et al., 2023 |
| Material flow analysis (n = 1) | Schmutz and Som, 2022 |
| Material flow analysis and carbon cost analysis (n = 1) | Millward-Hopkins et al., 2023 |
| Mathematical approach (n = 1) | Peters et al., 2021 |
| Scanning electron microscopy (n = 1) | Wesley et al., 2023 |
| Scenario development (n= 2) | Repp et al., 2021; Shou et al., 2022 |
| Social life cycle assessment (n = 1) | Martin and Herlaar, 2021 |
| Stakeholder analysis (n = 1) | Singh et al., 2019 |
| Survey (n = 11) | Dhir et al., 2021; Bhandari et al., 2022; Dangelico et al., 2022; de Oliveira et al., 2022; Fuchs and Hovemann, 2022; Johnstone and Lindh, 2022; Schmutz and Som, 2022; Xue et al., 2022; Amasawa et al., 2023; Pandey and Yadav, 2023; Scott et al., 2023 |
| Systematic analysis (n = 2) | Jain et al., 2021; Cooper and Claxton, 2022 |
| Systems model technique (Causal loop diagram (n = 1) | Singh et al., 2019 |
| Thematic analysis (n = 1) | Bocken and Konietzko, 2022 |
| Thermal transformation of waste cotton textile into carbon fibre (n = 1) | Wesley et al., 2023 |
| Thermo-gravimetric analysis (n = 1) | Wesley et al., 2023 |
| Two-round disaggregative Delphi approach (n = 1) | Louma et al., 2022 |
| X-ray diffraction (n = 1) | Wesley et al., 2023 |
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