Mobile shopping decision comfort using augmented reality: the effects of perceived augmentation and haptic imagery
Bibliographic record
Abstract
Purpose Virtual try-on apps (VTOs) allow consumers to examine fashion and furniture items in usage context without going to a physical store. But the adoption of such apps has varied across product categories, and research on user acceptance of AR marketing has been fragmented. The current study aims to develop and test a general model that explains the formation of decision comfort (DC) in the majority of AR try-on experiences for mobile shopping. Design/methodology/approach After reviewing 30 VTOs available on the iOS app store, the authors chose the Wanna Kicks sneaker shopping VTO as the most representative to test their hypotheses for AR try-on in general. Overall, 178 online consumers performed a sneaker shopping task on their mobile devices, and their responses were analyzed with the partial least squares method. Findings The study confirmed the key role of perceived augmentation in leading to DC via a utilitarian and a hedonic path. These effects were attenuated for younger users, and haptic imagery only had a utilitarian impact. Scholars should pay more attention to the variable of age, while managers should act quickly to enhance the basic AR affordances of mobile try-on apps. Originality/value This is the first study of a VTO in the footwear category and with a model that tests age as a moderating variable between antecedents and consumer responses.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".