A cross-national study of young female consumer behaviour, innovativeness and apparel evaluation: China and India
Bibliographic record
Abstract
In order to advance our knowledge about consumers’ shopping behaviour and preferences in two emerging markets (China and India), the current study was undertaken to investigate (1) apparel consumers’ shopping behaviour; (2) the effect of consumer innovativeness, and (3) the salient impact of apparel evaluative cues. An online self-administered survey consisted of shopping behavioural questions, the Domain Specific Innovativeness (DSI) scale, 12 apparel cues, and demographic questions were used for this study. In total, 266 and 236 usable data were collected from Chinese and Indian female participants respectively. The findings indicated that Chinese and Indian fashion innovators tended to spend more money on new clothes than non-innovators. Chinese fashion innovators spent significantly more time shopping online than did Indian innovators. In terms of the importance of evaluative cues, fashion innovators and non-innovators in both countries considered fit to be the most important cue; style, colour, and comfort played a relatively important role in clothing evaluation as well, but ease of care and durability were cited as relatively less important among many other cues. The two least important cues were brand name and country of origin.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".