A cross-national study of apparel consumer preferences and the role of product-evaluative cues
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate and identify the salient effects of apparel evaluativ1e cues in order to enrich our understanding of consumer preferences and behaviour in two different socio-cultural contexts – Canada from the west and China of the east. Design/methodology/approach Online and paper questionnaires were used to collect data from Canada and China. Based on the prior research, 14 hypotheses were developed, and SPSS statistical analyses were used for this study. Findings According to the findings, Canadian and Chinese participants used price as an indicator of product quality and/or monetary sacrifice. Overall, the consumers from both countries were more concerned about the garment fit and style than brand name and country of origin. It is imperative for fashion practitioners to prioritise their resources and focus more on product research/design and prototype development. Fit, comfort and fabric were strongly correlated except in the “fit and comfort” of the Canadian sample. In addition, durability, ease of care and wardrobe coordination play a relatively less significant clothing evaluative role than many other product cues. Originality/value There are relatively few research studies have focussed on apparel consumer behaviour, and the salient impact of product-evaluative cues – particularly from a cross-national perspective. This study covers a wide array of important evaluative cues, and provides meaningful insights to both fashion academicians and practitioners. This is one of the few studies provide an in-depth and comprehensive report on the role and effects of apparel product cues.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".