Enhancing the Online Shopping Experience: The Impact of Tactility in Augmented Reality
Bibliographic record
Abstract
Consumers encounter various difficulties when online shopping, such as receiving apparel that does not align with their fabric preference or does not fit as expected.Although online shopping facilitates the rapid finding of desired attire through search functions, it does not afford consumers the same comprehensive understanding of apparel information available in a physical retail setting.Particularly, it lacks the capability for consumers to physically experience the fabric through touch or to ascertain fit through trying on the apparel.This study investigates how tactile feedback can provide enhancement within Augmented Reality (AR) and alleviate the sensory constraints of online shopping, aiming to furnish a more immersive and information-rich consumer experience.The between-subjects experimental design replicated the online shopping experience by AR try-on with the incorporation of tactile sensations using supplementary fabric samples.The study concludes that tactile feedback significantly bolsters the authenticity of product presentations within AR environments, granting consumers a nuanced comprehension of attributes such as texture and fit.These insights were gained from a combination of unstructured interviews, observation, and questionnaires.The findings underscore the innovative application of tactile feedback in AR try-on technology, highlighting its role in bridging the gap between the convenience of online shopping and the rich sensory experience of traditional retail.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".