Factors Influencing Consumer Loyalty in Augmented Reality Beauty Apps: Sephora Virtual Artist Empirical Study
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
<p>With increase in usage of online mobile shopping apps, app developers and marketers are seeking innovative options to provide consumers with unique shopping experiences. In recent times, Virtual try-on feature in augmented reality apps have gained popularity. This study extends the electronic service quality model (ES-QUAL) with Hedonic Motivation and Perceived Value and seeks to explain the factors influencing consumer loyalty intentions in AR beauty apps. Sephora is a leading brand in the cosmetic industry and Virtual Artist is its augmented reality try-on feature. An online survey was conducted with 251 university students. PLS-SEM analysis results suggest that Hedonic Motivation and Efficiency significantly impact loyalty intentions, while Perceived value, Perceived Privacy Risks, System Availability and Fulfilment were not significant. This study contributes to the existing literature in the domain of consumers loyalty intentions and AR apps. Retail practitioners can use the results to boost consumer loyalty and predict purchase intention. </p>
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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