The Impact of Visual Complexity on the Elderly and Young Consumers: Browsing in a Clothing Store
Bibliographic record
Abstract
The aging of the world's population will have a considerable impact on the consumer market. Basically, the elderly is less able to accept online purchases than young people. Therefore, many elderly consumers are still accustomed to buying in physical stores. The spatial visual complexity of the store may have an impact on the elderly consumers’ perception, which in turn affects their shopping emotions and behavior. The primary purpose of this study is to empirically explore how visual complexity affects consumers’ perception and psychology in a retail environment; and to further explore the effects on the experiences of the elderly and young consumers. The findings of this study indicate that the combinations of spatial layout and pattern decoration have different influences on the elderly and young consumers’ perceptions and psychological responses in a clothing store. The elderly consumers believe that the higher visual complexity, the more easily their attention is disturbed, and they do not like shopping in the highest spatial visual complexity. In terms of the impact of visual complexity on pleasant emotions, the elderly consumers have stronger impact than the young consumers. The finding suggests that if retailers want the elderly consumers to concentrate on searching for merchandise, they can simplify spatial layouts and decoration patterns to reduce the visual complexity of the space.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".