A Comprehensive Study of Sustainability, Fashion and Consumption: Understanding Consumer Behaviour and Clothing Evaluation from a Cross-national Perspective
Bibliographic record
Abstract
The overarching objectives of this research are twofold: (1) gaining an in-depth understanding of the current trends and developments in “fashion and sustainability” research between 2010 and 2021, identifying research gaps and influential works; and (2) examining the influence of sustainable and non-sustainable product attributes on apparel consumer choices from a cross-national perspective, specifically focusing on Indian and Canadian consumers. Although previous research has explored fashion and sustainability, to the best of my knowledge, no study has undertaken a comprehensive examination of the aforementioned objectives. Prior to this study, no research has been conducted on the significance of (non-)sustainable evaluative cues from the perspectives of both Indian and Canadian apparel consumers. \nThis thesis comprises six chapters that unfold as follows: Chapter 1 establishes the contextual background and research rationale for the topic. Chapter 2 conducts a systematic literature review to provide a comprehensive overview of the research conducted and disseminated on “fashion and sustainability” since 2010. To facilitate data analysis and gain a deeper understanding of the research evolution, various free open-access software tools such as RAKE, VOSviewer and CitNetExplorer were employed. Boolean queries were utilised to search and retrieve 860 articles related to “fashion and sustainability” from the Web of Science. Based on these publications, the research’s geographic distribution, types and approaches (e.g., qualitative, quantitative, case study and systematic literature review), author’s affiliations by country, publication trends, number of cross-national/cultural studies, and major sustainable and non-sustainable attributes were identified and examined. Chapter 3 delves into an extensive literature review of evaluative attributes in apparel, encompassing both sustainable and non-sustainable cues. This review provides insights into consumer buying behaviour across various dimensions, such as sustainable cues versus non-sustainable cues, differences between male and female consumers, and variations between developed and developing nations. Importantly, this information informs the formulation of relevant hypotheses for empirical testing in the subsequent chapter. The chapter also presents and discusses the typology of apparel cues, including intrinsic and extrinsic cues, psychic/aesthetic and physical/functional cues, sustainable and non-sustainable cues and “product-related cues and production-related cues. Chapter 4 outlines the research methodology employed for empirical testing and analysis. Data collection was conducted through self-administered online survey, with various measurement instruments developed based on prior literature. The collected data were analysed using SPSS, employing techniques such as descriptive analysis, t-test and correlation test. Chapter 5 presents the analytical results and discusses the salient impact of various apparel product cues. Additionally, it reports on the differences observed between Canadian and Indian consumers, as well as gender disparities in clothing choice. The final chapter summarised the findings, highlighted the contributions, implications and limitation of the current research, and provides recommendations for future research areas that warrant further investigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".