The shopping experience of female fashion leaders
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how the shopping mall environment impacts on hedonic and utilitarian shopping experiences, and approach behaviour of fashion leaders and followers. Design/methodology/approach Fashion shoppers' response and behaviour has been modelled in an invariant multigroup latent structural path analysis. Paths were initially constrained and then released as required. More than 300 usable questionnaires were acquired from a mall intercept in a regional urban middleclass shopping centre. Participants were probed on their attitude about fashion, perception of the shopping mall, present mood, shopping value and approach behaviour toward the mall. Findings The mall environment directly influences fashion leaders' hedonic shopping experience and approach behaviour. Fashion followers' hedonic shopping experience may be mood driven, while that of fashion leaders' is triggered by higher involvement cognitive processing. Research limitations/implications This study was carried out in one fashion‐oriented urban mall in Montreal, and should be replicated to other locations and markets. A larger sample would allow the inclusion of additional constructs. Practical implications Mall owners and developers might appeal to fashion leaders through offering services that will speed up their shopping trip, using high‐tech methods to convey fashion information and by branding the mall. Fashion followers and laggards are likely to respond to experience‐oriented strategies that make their shopping trip more pleasurable. Originality/value Although fashion consumer groups have been studied from various perspectives, no research was found that investigates the integrated shopping experience of fashion shoppers in a shopping mall setting. This study fills the void.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".