Bibliographic record
Abstract
Apparel mass-customization is a relatively new service and product concept, which engages consumers in co-designing clothing online for a tailored product.This study explores Canadian consumers' attitudes towards clothing, custom clothing, and co-design apparel online, and assesses the potential for the adoption of this concept in Canada.This study is guided by multi-component attitude model, where attitudes are formed by cognitive, affective, and prior experiences.The findings indicate that Canadian participants have a positive attitude towards custom clothing, and the co-design process.Results show that the sample was ready to adopt co-designing clothing online, provided they trusted the service, had access to good interactive and efficient online tools, a variety of design options, and design help.The majority of the sample was willing to pay more, and willing to wait longer for their co-designed clothing.Each of these findings is examined by a multi-layer consumer segmentation of gender, social tendency and shopping behaviour.The study uses two sets of sources, and two data collection methods.Sources included 13 adult Canadian consumers, and 11 industry experts from Canada, United States, Western Europe, and Australia.Data collection methods included a semi-structured interview, and a questionnaire.Consumer responses are compared with industry experts for validity and to provide a 360° perspective.Consumer and expert responses are compared and contrasted with relevant academic literature.The study contributes to the understanding of Canadian consumer motivations, and the potential for adopting apparel mass-customization in specific contexts.Findings are discussed in light of their relevance to apparel and user experience designers, marketers, and academic researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".