Mobile shopping decision comfort using augmented reality: the effects of perceived augmentation and haptic imagery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it