How online fashion videos affect consumer’s brand perceptions: an exhibition of academic thesis.
Bibliographic record
Abstract
The objective of this research is to explore how online fashion videos affect consumers’ perception toward fashion brands. This study builds upon research in cognitive processing, attitude formation and interactive online technology. This study used optometric or gaze tracking to follow what participants focused upon when exposed to fashion videos. All participants were female, half in fashion related programs and the other half in non-fashion, non-design related programs. A semi-structured interview, visual stimuli (video), and pre-/post-questionnaire were used. The study found that participants did not fully remember videos to which they have been exposed to or content upon which they had focused. However, it is important to note that participants could recall a considerable amount of information when their eye pupils dilated during viewing. Although participants’ perceptions toward video did not show significant changes after they found out the brand name, they tended to use different words or vocabularies from the pre-questionnaire survey to describe the brand image. It is evident that the relationship between pupil dilation and memory recall is positive. As this study deals with perceptions, further investigation into participant’s memories and associations with visual attributes will provide additional considerations, particularly how associations are made and recalled by viewers over time and after exposed to a brand’s messages over a period of time.
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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 | 0.000 | 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) | 0.009 | 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".